Ant_Colony_Optimization_6.ipynb

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      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "***APPLICATION IN ELECTRONICS***\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "dP6LG2ImQwDc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`CMOS Amplifier Optimization`**"
      ],
      "metadata": {
        "id": "pIAjcEwaQ2eZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "NUM_ANTS = 30\n",
        "NUM_ITERATIONS = 300\n",
        "ALPHA = 1.0\n",
        "BETA = 2.0\n",
        "RHO = 0.1\n",
        "Q0 = 0.7\n",
        "PHEROMONE_MIN = 0.01\n",
        "PHEROMONE_MAX = 1.0\n",
        "NUM_SENSORS = 15\n",
        "NUM_TRANSISTORS = 25\n",
        "MAX_SENSOR_VALUE = 10\n",
        "MAX_POWER_CONSUMPTION = 100.0\n",
        "TARGET_DELAY = 50.0\n",
        "def evaluate_solution(solution, power_consumption, delay):\n",
        "    total_sensor_product = np.prod(solution)\n",
        "    total_power_consumption = np.sum(power_consumption)\n",
        "    total_delay = np.sum(delay)\n",
        "    power_penalty = max(0.0, total_power_consumption - MAX_POWER_CONSUMPTION)\n",
        "    delay_penalty = max(0.0, total_delay - TARGET_DELAY)\n",
        "    objective = total_sensor_product - 0.5 * power_penalty - 0.5 * delay_penalty\n",
        "    return objective\n",
        "def update_pheromones(pheromones, delta_pheromones, ant, solution, solution_quality):\n",
        "    evaporation = RHO\n",
        "    for i in range(NUM_TRANSISTORS):\n",
        "        sensor = solution[i]\n",
        "        delta_pheromones[sensor][i] += (1.0 - evaporation) * (Q0 / solution_quality)\n",
        "    pheromones = np.clip(pheromones * evaporation + delta_pheromones, PHEROMONE_MIN, PHEROMONE_MAX)\n",
        "    return pheromones\n",
        "def ant_colony_optimization(pheromones, power_consumption, delay):\n",
        "    best_objective = -1e9\n",
        "    for iteration in range(NUM_ITERATIONS):\n",
        "        delta_pheromones = np.zeros((NUM_SENSORS, NUM_TRANSISTORS))\n",
        "        best_iteration_objective = -1e9\n",
        "        for ant in range(NUM_ANTS):\n",
        "            ant_solution = np.zeros(NUM_TRANSISTORS, dtype=int)\n",
        "            for i in range(NUM_TRANSISTORS):\n",
        "                rand_value = np.random.rand()\n",
        "                if rand_value < Q0:\n",
        "                    best_sensor = np.argmax(pheromones[:, i])\n",
        "                    ant_solution[i] = best_sensor\n",
        "                else:\n",
        "                    total_probability = np.sum(np.power(pheromones[:, i], ALPHA) * np.power(1.0 / (np.arange(NUM_SENSORS) + 1), BETA))\n",
        "                    random_value = np.random.rand()\n",
        "                    cumulative_probability = 0.0\n",
        "                    selected_sensor = 0\n",
        "                    for s in range(NUM_SENSORS):\n",
        "                        cumulative_probability += (np.power(pheromones[s, i], ALPHA) * np.power(1.0 / (s + 1), BETA)) / total_probability\n",
        "                        if random_value <= cumulative_probability:\n",
        "                            selected_sensor = s\n",
        "                            break\n",
        "                    ant_solution[i] = selected_sensor\n",
        "            solution_quality = evaluate_solution(ant_solution, power_consumption, delay)\n",
        "            pheromones = update_pheromones(pheromones, delta_pheromones, ant, ant_solution, solution_quality)\n",
        "            if solution_quality > best_iteration_objective:\n",
        "                best_iteration_objective = solution_quality\n",
        "        if best_iteration_objective > best_objective:\n",
        "            best_objective = best_iteration_objective\n",
        "            print(f\"Iteration {iteration + 1}: Best Objective = {best_objective:.2f}\")\n",
        "pheromones = np.ones((NUM_SENSORS, NUM_TRANSISTORS))\n",
        "power_consumption = np.array([15.0, 12.0, 18.0, 10.0, 20.0, 14.0, 16.0, 22.0, 25.0, 30.0, 8.0, 17.0, 19.0, 21.0, 24.0, 13.0, 11.0, 23.0, 28.0, 26.0, 9.0, 27.0, 7.0, 29.0, 31.0])\n",
        "delay = np.array([45.0, 48.0, 42.0, 50.0, 40.0, 46.0, 44.0, 38.0, 35.0, 30.0, 52.0, 43.0, 41.0, 39.0, 36.0, 47.0, 49.0, 37.0, 32.0, 34.0, 51.0, 33.0, 53.0, 31.0, 29.0])\n",
        "ant_colony_optimization(pheromones, power_consumption, delay)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPEnMJIhTB7F",
        "outputId": "3a9df702-9beb-4186-918a-8c6f4f33a3d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration 1: Best Objective = -675.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "NUM_ANTS = 30\n",
        "NUM_ITERATIONS = 300\n",
        "ALPHA = 1.0\n",
        "BETA = 2.0\n",
        "RHO = 0.1\n",
        "MIN_Q0 = 0.1\n",
        "MAX_Q0 = 0.9\n",
        "PHEROMONE_MIN = 0.01\n",
        "PHEROMONE_MAX = 1.0\n",
        "NUM_SENSORS = 15\n",
        "NUM_TRANSISTORS = 25\n",
        "MAX_SENSOR_VALUE = 10\n",
        "MAX_POWER_CONSUMPTION = 100.0\n",
        "TARGET_DELAY = 50.0\n",
        "def evaluate_solution(solution, power_consumption, delay):\n",
        "    total_sensor_product = np.prod(solution)\n",
        "    total_power_consumption = np.sum(power_consumption)\n",
        "    total_delay = np.sum(delay)\n",
        "    power_penalty = max(0.0, total_power_consumption - MAX_POWER_CONSUMPTION)\n",
        "    delay_penalty = max(0.0, total_delay - TARGET_DELAY)\n",
        "    objective = total_sensor_product - 0.5 * power_penalty - 0.5 * delay_penalty\n",
        "    return objective\n",
        "def update_pheromones(pheromones, delta_pheromones, ant, solution, solution_quality, local_update=True):\n",
        "    evaporation = RHO\n",
        "    q0 = MIN_Q0 + (MAX_Q0 - MIN_Q0) * (NUM_ITERATIONS - ant) / NUM_ITERATIONS\n",
        "    for i in range(NUM_TRANSISTORS):\n",
        "        sensor = solution[i]\n",
        "        delta_pheromones[sensor][i] += (1.0 - evaporation) * (q0 / solution_quality)\n",
        "        if local_update:\n",
        "            pheromones[sensor][i] = np.clip(pheromones[sensor][i] * (1.0 - evaporation) + delta_pheromones[sensor][i], PHEROMONE_MIN, PHEROMONE_MAX)\n",
        "    pheromones = np.clip(pheromones * evaporation + delta_pheromones, PHEROMONE_MIN, PHEROMONE_MAX)\n",
        "    return pheromones\n",
        "def ant_colony_optimization(pheromones, power_consumption, delay):\n",
        "    best_objective = -1e9\n",
        "    best_solution = None\n",
        "    convergence_curve = []\n",
        "    for iteration in range(NUM_ITERATIONS):\n",
        "        delta_pheromones = np.zeros((NUM_SENSORS, NUM_TRANSISTORS))\n",
        "        best_iteration_objective = -1e9\n",
        "        best_iteration_solution = None\n",
        "        for ant in range(NUM_ANTS):\n",
        "            ant_solution = np.zeros(NUM_TRANSISTORS, dtype=int)\n",
        "            for i in range(NUM_TRANSISTORS):\n",
        "                rand_value = np.random.rand()\n",
        "                if rand_value < q0:\n",
        "                    best_sensor = np.argmax(pheromones[:, i])\n",
        "                    ant_solution[i] = best_sensor\n",
        "                else:\n",
        "                    total_probability = np.sum(np.power(pheromones[:, i], ALPHA) * np.power(1.0 / (np.arange(NUM_SENSORS) + 1), BETA))\n",
        "                    random_value = np.random.rand()\n",
        "                    cumulative_probability = 0.0\n",
        "                    selected_sensor = 0\n",
        "                    for s in range(NUM_SENSORS):\n",
        "                        cumulative_probability += (np.power(pheromones[s, i], ALPHA) * np.power(1.0 / (s + 1), BETA)) / total_probability\n",
        "                        if random_value <= cumulative_probability:\n",
        "                            selected_sensor = s\n",
        "                            break\n",
        "                    ant_solution[i] = selected_sensor\n",
        "            solution_quality = evaluate_solution(ant_solution, power_consumption, delay)\n",
        "            pheromones = update_pheromones(pheromones, delta_pheromones, ant, ant_solution, solution_quality)\n",
        "            if solution_quality > best_iteration_objective:\n",
        "                best_iteration_objective = solution_quality\n",
        "                best_iteration_solution = ant_solution\n",
        "        convergence_curve.append(best_iteration_objective)\n",
        "        if best_iteration_objective > best_objective:\n",
        "            best_objective = best_iteration_objective\n",
        "            best_solution = best_iteration_solution\n",
        "            print(f\"Best Objective = {best_objective:.2f}\")\n",
        "            print(\"Best Solution (Sensor Choices) =\", best_solution)\n",
        "            print(\"Power Consumption =\", np.sum(power_consumption))\n",
        "            print(\"Delay =\", np.sum(delay))\n",
        "            print(\"\\n\")\n",
        "pheromones = np.random.rand(NUM_SENSORS, NUM_TRANSISTORS)\n",
        "power_consumption = np.array([15.0, 12.0, 18.0, 10.0, 20.0, 14.0, 16.0, 22.0, 25.0, 30.0, 8.0, 17.0, 19.0, 21.0, 24.0, 13.0, 11.0, 23.0, 28.0, 26.0, 9.0, 27.0, 7.0, 29.0, 31.0])\n",
        "delay = np.array([45.0, 48.0, 42.0, 50.0, 40.0, 46.0, 44.0, 38.0, 35.0, 30.0, 52.0, 43.0, 41.0, 39.0, 36.0, 47.0, 49.0, 37.0, 32.0, 34.0, 51.0, 33.0, 53.0, 31.0, 29.0])\n",
        "ant_colony_optimization(pheromones, power_consumption, delay)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTR-WUTaUc1W",
        "outputId": "f124045e-b174-4607-b98c-43f341848422"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Objective = -675.00\n",
            "Best Solution (Sensor Choices) = [ 1  3 12  3  3  7  2  0  6 14 10  3  2 14  9  5  1  5  6 12  1  5  7 10\n",
            " 13]\n",
            "Power Consumption = 475.0\n",
            "Delay = 1025.0\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install libgraphviz-dev\n",
        "!pip install pygraphviz"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_-rlZuiVNYi",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pygraphviz as pgv\n",
        "import matplotlib.pyplot as plt\n",
        "NUM_ANTS = 30\n",
        "NUM_ITERATIONS = 300\n",
        "ALPHA = 1.0\n",
        "BETA = 2.0\n",
        "RHO = 0.1\n",
        "MIN_Q0 = 0.1\n",
        "MAX_Q0 = 0.9\n",
        "PHEROMONE_MIN = 0.01\n",
        "PHEROMONE_MAX = 1.0\n",
        "NUM_SENSORS = 15\n",
        "NUM_TRANSISTORS = 25\n",
        "MAX_SENSOR_VALUE = 10\n",
        "MAX_POWER_CONSUMPTION = 100.0\n",
        "TARGET_DELAY = 50.0\n",
        "def evaluate_solution(solution, power_consumption, delay):\n",
        "    total_sensor_product = np.prod(solution)\n",
        "    total_power_consumption = np.sum(power_consumption)\n",
        "    total_delay = np.sum(delay)\n",
        "    power_penalty = max(0.0, total_power_consumption - MAX_POWER_CONSUMPTION)\n",
        "    delay_penalty = max(0.0, total_delay - TARGET_DELAY)\n",
        "    objective = total_sensor_product - 0.5 * power_penalty - 0.5 * delay_penalty\n",
        "    return objective\n",
        "def update_pheromones(pheromones, delta_pheromones, ant, solution, solution_quality, local_update=True):\n",
        "    evaporation = RHO\n",
        "    q0 = MIN_Q0 + (MAX_Q0 - MIN_Q0) * (NUM_ITERATIONS - ant) / NUM_ITERATIONS\n",
        "    for i in range(NUM_TRANSISTORS):\n",
        "        sensor = solution[i]\n",
        "        delta_pheromones[sensor][i] += (1.0 - evaporation) * (q0 / solution_quality)\n",
        "        if local_update:\n",
        "            pheromones[sensor][i] = np.clip(pheromones[sensor][i] * (1.0 - evaporation) + delta_pheromones[sensor][i], PHEROMONE_MIN, PHEROMONE_MAX)\n",
        "    pheromones = np.clip(pheromones * evaporation + delta_pheromones, PHEROMONE_MIN, PHEROMONE_MAX)\n",
        "    return pheromones\n",
        "def ant_colony_optimization(pheromones, power_consumption, delay):\n",
        "    graph = pgv.AGraph(directed=True)\n",
        "    for i in range(NUM_TRANSISTORS):\n",
        "        graph.add_node(i, label=f\"Transistor {i+1}\")\n",
        "    best_objective = -1e9\n",
        "    best_solution = None\n",
        "    convergence_curve = []\n",
        "    for iteration in range(NUM_ITERATIONS):\n",
        "        delta_pheromones = np.zeros((NUM_SENSORS, NUM_TRANSISTORS))\n",
        "        best_iteration_objective = -1e9\n",
        "        best_iteration_solution = None\n",
        "        for ant in range(NUM_ANTS):\n",
        "            ant_solution = np.zeros(NUM_TRANSISTORS, dtype=int)\n",
        "            for i in range(NUM_TRANSISTORS):\n",
        "                rand_value = np.random.rand()\n",
        "                if rand_value < q0:\n",
        "                    best_sensor = np.argmax(pheromones[:, i])\n",
        "                    ant_solution[i] = best_sensor\n",
        "                else:\n",
        "                    total_probability = np.sum(np.power(pheromones[:, i], ALPHA) * np.power(1.0 / (np.arange(NUM_SENSORS) + 1), BETA))\n",
        "                    random_value = np.random.rand()\n",
        "                    cumulative_probability = 0.0\n",
        "                    selected_sensor = 0\n",
        "                    for s in range(NUM_SENSORS):\n",
        "                        cumulative_probability += (np.power(pheromones[s, i], ALPHA) * np.power(1.0 / (s + 1), BETA)) / total_probability\n",
        "                        if random_value <= cumulative_probability:\n",
        "                            selected_sensor = s\n",
        "                            break\n",
        "                    ant_solution[i] = selected_sensor\n",
        "            solution_quality = evaluate_solution(ant_solution, power_consumption, delay)\n",
        "            pheromones = update_pheromones(pheromones, delta_pheromones, ant, ant_solution, solution_quality)\n",
        "            if solution_quality > best_iteration_objective:\n",
        "                best_iteration_objective = solution_quality\n",
        "                best_iteration_solution = ant_solution\n",
        "        convergence_curve.append(best_iteration_objective)\n",
        "        if best_iteration_objective > best_objective:\n",
        "            best_objective = best_iteration_objective\n",
        "            best_solution = best_iteration_solution\n",
        "            for i, sensor in enumerate(best_solution):\n",
        "                graph.add_edge(sensor, i, label=f\"Iteration {iteration+1}\")\n",
        "    graph.layout(prog='dot')\n",
        "    graph.draw('CMOS_Amplifier_Optmization.png')\n",
        "    img = plt.imread('CMOS_Amplifier_Optmization.png')\n",
        "    plt.imshow(img)\n",
        "    plt.show()\n",
        "pheromones = np.random.rand(NUM_SENSORS, NUM_TRANSISTORS)\n",
        "power_consumption = np.array([15.0, 12.0, 18.0, 10.0, 20.0, 14.0, 16.0, 22.0, 25.0, 30.0, 8.0, 17.0, 19.0, 21.0, 24.0, 13.0, 11.0, 23.0, 28.0, 26.0, 9.0, 27.0, 7.0, 29.0, 31.0])\n",
        "delay = np.array([45.0, 48.0, 42.0, 50.0, 40.0, 46.0, 44.0, 38.0, 35.0, 30.0, 52.0, 43.0, 41.0, 39.0, 36.0, 47.0, 49.0, 37.0, 32.0, 34.0, 51.0, 33.0, 53.0, 31.0, 29.0])\n",
        "ant_colony_optimization(pheromones, power_consumption, delay)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "cGfQtV18VW7_",
        "outputId": "4386fe0f-fe9e-4a4d-8c1f-7d7cc62699d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def objective_function(parameters):\n",
        "    return parameters[0]**2 + parameters[1]**2\n",
        "def ant_colony_optimization(iterations, num_ants, num_parameters):\n",
        "    best_solution = None\n",
        "    best_cost = float('inf')\n",
        "    pheromones = np.ones(num_parameters)\n",
        "    for _ in range(iterations):\n",
        "        solutions = np.random.rand(num_ants, num_parameters)\n",
        "        costs = np.apply_along_axis(objective_function, 1, solutions)\n",
        "        pheromones = pheromones * 0.9 + 0.1 / (costs.reshape(-1, 1) + 1e-5)\n",
        "        best_ant = np.argmin(costs)\n",
        "        if costs[best_ant] < best_cost:\n",
        "            best_cost = costs[best_ant]\n",
        "            best_solution = solutions[best_ant]\n",
        "    return best_solution, best_cost\n",
        "iterations = 100\n",
        "num_ants = 10\n",
        "num_parameters = 2\n",
        "best_solution, best_cost = ant_colony_optimization(iterations, num_ants, num_parameters)\n",
        "print(f\"Optimal Solution = {best_solution}\")\n",
        "print(f\"Optimal Cost = {best_cost}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unQt-uLcWcQ9",
        "outputId": "d8c99a9b-dbc2-435b-d357-5bec98b3ccfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal Solution = [0.03070522 0.02578754]\n",
            "Optimal Cost = 0.0016078074757528604\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "def objective_function(parameters):\n",
        "    return parameters[0]**2 + parameters[1]**2\n",
        "def ant_colony_optimization(iterations, num_ants, num_parameters):\n",
        "    best_solution = None\n",
        "    best_cost = float('inf')\n",
        "    convergence_curve = []\n",
        "    pheromones = np.ones((num_ants, num_parameters))\n",
        "    pheromone_history = np.zeros((iterations + 1, num_parameters))\n",
        "    pheromone_history[0, :] = np.mean(pheromones, axis=0)\n",
        "    for iteration in range(iterations):\n",
        "        solutions = np.random.rand(num_ants, num_parameters)\n",
        "        costs = np.apply_along_axis(objective_function, 1, solutions)\n",
        "        pheromones = pheromones * 0.9 + 0.1 / (costs.reshape(-1, 1) + 1e-5)\n",
        "        pheromone_history[iteration + 1, :] = np.mean(pheromones, axis=0)\n",
        "        best_ant = np.argmin(costs)\n",
        "        if costs[best_ant] < best_cost:\n",
        "            best_cost = costs[best_ant]\n",
        "            best_solution = solutions[best_ant]\n",
        "        convergence_curve.append(best_cost)\n",
        "    return best_solution, best_cost, convergence_curve, pheromone_history\n",
        "iterations = 100\n",
        "num_ants = 10\n",
        "num_parameters = 2\n",
        "best_solution, best_cost, convergence_curve, pheromone_history = ant_colony_optimization(iterations, num_ants, num_parameters)\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(convergence_curve)\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Objective Function Value')\n",
        "plt.title('Convergence Curve')\n",
        "plt.subplot(1, 2, 2)\n",
        "for i in range(num_parameters):\n",
        "    plt.plot(pheromone_history[:, i], label=f'Parameter {i+1}')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Pheromone Level')\n",
        "plt.title('Pheromone Evolution')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "print(f\"Optimal Solution = {best_solution}\")\n",
        "print(f\"Optimal Cost = {best_cost}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "5prz6zEkXQgp",
        "outputId": "1f64f03f-e497-4710-dfb6-96413d294630"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal Solution = [0.00580931 0.03737353]\n",
            "Optimal Cost = 0.0014305290383181488\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Reversible Circuit Synthesis `**"
      ],
      "metadata": {
        "id": "uHcBHgHWM_Om"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import math\n",
        "NUM_ANTS = 10\n",
        "NUM_ITERATIONS = 100\n",
        "ALPHA = 1.0\n",
        "BETA = 2.0\n",
        "RHO = 0.5\n",
        "Q = 100\n",
        "Q0 = 0.9\n",
        "NUM_NODES = 6\n",
        "class Node:\n",
        "    def __init__(self, value):\n",
        "        self.value = value\n",
        "class Edge:\n",
        "    def __init__(self, source, destination, graph):\n",
        "        self.source = source\n",
        "        self.destination = destination\n",
        "        self.pheromone = 1.0\n",
        "        node_value_difference = abs(graph.nodes[source].value - graph.nodes[destination].value)\n",
        "        self.heuristic = 1.0 / node_value_difference if node_value_difference != 0 else float('inf')\n",
        "class Graph:\n",
        "    def __init__(self):\n",
        "        self.num_nodes = NUM_NODES\n",
        "        self.num_edges = NUM_NODES * (NUM_NODES - 1) // 2\n",
        "        self.nodes = [Node(random.randint(0, 99)) for _ in range(self.num_nodes)]\n",
        "        self.edges = [Edge(i, j, self) for i in range(self.num_nodes - 1) for j in range(i + 1, self.num_nodes)]\n",
        "class Ant:\n",
        "    def __init__(self):\n",
        "        self.tour = []\n",
        "        self.tour_length = 0.0\n",
        "def initialize_pheromones(graph):\n",
        "    for edge in graph.edges:\n",
        "        edge.pheromone = 1.0\n",
        "def generate_random_tour(graph, ant):\n",
        "    ant.tour_length = 0\n",
        "    ant.tour = list(range(graph.num_nodes))\n",
        "    for i in range(graph.num_nodes - 1, 0, -1):\n",
        "        j = random.randint(0, i)\n",
        "        ant.tour[i], ant.tour[j] = ant.tour[j], ant.tour[i]\n",
        "def calculate_tour_length(graph, ant):\n",
        "    tour_length = 0.0\n",
        "    for i in range(graph.num_nodes - 1):\n",
        "        source = ant.tour[i]\n",
        "        destination = ant.tour[i + 1]\n",
        "        tour_length += abs(graph.nodes[source].value - graph.nodes[destination].value)\n",
        "    last_node = ant.tour[graph.num_nodes - 1]\n",
        "    tour_length += abs(graph.nodes[last_node].value - graph.nodes[ant.tour[0]].value)\n",
        "    return tour_length\n",
        "def contains_node(tour, node):\n",
        "    return node in tour\n",
        "def update_pheromones(graph, delta_pheromones):\n",
        "    for i in range(graph.num_edges):\n",
        "        for j in range(NUM_ANTS):\n",
        "            graph.edges[i].pheromone *= (1 - RHO)\n",
        "            graph.edges[i].pheromone += delta_pheromones[i].pheromone\n",
        "def choose_next_node(graph, ant, pheromones, heuristics, exploration_prob):\n",
        "    current = ant.tour[-1]\n",
        "    probabilities = [0.0] * graph.num_nodes\n",
        "    total_probability = 0.0\n",
        "    for i in range(graph.num_nodes):\n",
        "        if not contains_node(ant.tour, i):\n",
        "            probabilities[i] = (\n",
        "                math.pow(pheromones[current][i].pheromone, ALPHA) *\n",
        "                math.pow(heuristics[current][i].heuristic, BETA)\n",
        "            )\n",
        "            total_probability += probabilities[i]\n",
        "        else:\n",
        "            probabilities[i] = 0.0\n",
        "    if random.random() < exploration_prob:\n",
        "        random_value = random.random() * total_probability\n",
        "        cumulative_probability = 0.0\n",
        "        for i in range(graph.num_nodes):\n",
        "            if cumulative_probability + probabilities[i] >= random_value:\n",
        "                return i\n",
        "            cumulative_probability += probabilities[i]\n",
        "    else:\n",
        "        max_probability = -1.0\n",
        "        next_node = -1\n",
        "        for i in range(graph.num_nodes):\n",
        "            if probabilities[i] > max_probability:\n",
        "                max_probability = probabilities[i]\n",
        "                next_node = i\n",
        "        return next_node\n",
        "    return -1\n",
        "def ant_colony_optimization(graph):\n",
        "    delta_pheromones = [Edge(0, 0, graph) for _ in range(graph.num_edges)]\n",
        "    ants = [Ant() for _ in range(NUM_ANTS)]\n",
        "    for iteration in range(NUM_ITERATIONS):\n",
        "        for ant_index in range(NUM_ANTS):\n",
        "            generate_random_tour(graph, ants[ant_index])\n",
        "            while ants[ant_index].tour_length < graph.num_nodes:\n",
        "                next_node = choose_next_node(\n",
        "                    graph, ants[ant_index], graph.edges, graph.edges, Q0\n",
        "                )\n",
        "                ants[ant_index].tour.append(next_node)\n",
        "                ants[ant_index].tour_length += 1\n",
        "        best_tour_length = float('inf')\n",
        "        best_ant_index = -1\n",
        "        for ant_index in range(NUM_ANTS):\n",
        "            ants[ant_index].tour_length = calculate_tour_length(\n",
        "                graph, ants[ant_index]\n",
        "            )\n",
        "            if ants[ant_index].tour_length < best_tour_length:\n",
        "                best_tour_length = ants[ant_index].tour_length\n",
        "                best_ant_index = ant_index\n",
        "        print(f\"Iteration-{iteration + 1}: Best Tour Length = {best_tour_length}\")\n",
        "        for i in range(graph.num_edges):\n",
        "            delta_pheromones[i].pheromone = Q / ants[best_ant_index].tour_length\n",
        "        update_pheromones(graph, delta_pheromones)\n",
        "if __name__ == \"__main__\":\n",
        "    random.seed()\n",
        "    graph = Graph()\n",
        "    initialize_pheromones(graph)\n",
        "    ant_colony_optimization(graph)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bvi9cQbjOgk6",
        "outputId": "70bb4b0c-9736-4486-bb18-aa7227f70e81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration-1: Best Tour Length = 178.0\n",
            "Iteration-2: Best Tour Length = 166.0\n",
            "Iteration-3: Best Tour Length = 192.0\n",
            "Iteration-4: Best Tour Length = 166.0\n",
            "Iteration-5: Best Tour Length = 166.0\n",
            "Iteration-6: Best Tour Length = 166.0\n",
            "Iteration-7: Best Tour Length = 178.0\n",
            "Iteration-8: Best Tour Length = 166.0\n",
            "Iteration-9: Best Tour Length = 166.0\n",
            "Iteration-10: Best Tour Length = 166.0\n",
            "Iteration-11: Best Tour Length = 166.0\n",
            "Iteration-12: Best Tour Length = 166.0\n",
            "Iteration-13: Best Tour Length = 166.0\n",
            "Iteration-14: Best Tour Length = 166.0\n",
            "Iteration-15: Best Tour Length = 204.0\n",
            "Iteration-16: Best Tour Length = 166.0\n",
            "Iteration-17: Best Tour Length = 166.0\n",
            "Iteration-18: Best Tour Length = 192.0\n",
            "Iteration-19: Best Tour Length = 166.0\n",
            "Iteration-20: Best Tour Length = 166.0\n",
            "Iteration-21: Best Tour Length = 166.0\n",
            "Iteration-22: Best Tour Length = 166.0\n",
            "Iteration-23: Best Tour Length = 204.0\n",
            "Iteration-24: Best Tour Length = 166.0\n",
            "Iteration-25: Best Tour Length = 166.0\n",
            "Iteration-26: Best Tour Length = 166.0\n",
            "Iteration-27: Best Tour Length = 204.0\n",
            "Iteration-28: Best Tour Length = 178.0\n",
            "Iteration-29: Best Tour Length = 166.0\n",
            "Iteration-30: Best Tour Length = 166.0\n",
            "Iteration-31: Best Tour Length = 192.0\n",
            "Iteration-32: Best Tour Length = 166.0\n",
            "Iteration-33: Best Tour Length = 192.0\n",
            "Iteration-34: Best Tour Length = 166.0\n",
            "Iteration-35: Best Tour Length = 166.0\n",
            "Iteration-36: Best Tour Length = 166.0\n",
            "Iteration-37: Best Tour Length = 166.0\n",
            "Iteration-38: Best Tour Length = 166.0\n",
            "Iteration-39: Best Tour Length = 166.0\n",
            "Iteration-40: Best Tour Length = 166.0\n",
            "Iteration-41: Best Tour Length = 178.0\n",
            "Iteration-42: Best Tour Length = 166.0\n",
            "Iteration-43: Best Tour Length = 166.0\n",
            "Iteration-44: Best Tour Length = 166.0\n",
            "Iteration-45: Best Tour Length = 166.0\n",
            "Iteration-46: Best Tour Length = 166.0\n",
            "Iteration-47: Best Tour Length = 166.0\n",
            "Iteration-48: Best Tour Length = 166.0\n",
            "Iteration-49: Best Tour Length = 166.0\n",
            "Iteration-50: Best Tour Length = 166.0\n",
            "Iteration-51: Best Tour Length = 166.0\n",
            "Iteration-52: Best Tour Length = 166.0\n",
            "Iteration-53: Best Tour Length = 166.0\n",
            "Iteration-54: Best Tour Length = 166.0\n",
            "Iteration-55: Best Tour Length = 166.0\n",
            "Iteration-56: Best Tour Length = 166.0\n",
            "Iteration-57: Best Tour Length = 166.0\n",
            "Iteration-58: Best Tour Length = 166.0\n",
            "Iteration-59: Best Tour Length = 178.0\n",
            "Iteration-60: Best Tour Length = 192.0\n",
            "Iteration-61: Best Tour Length = 166.0\n",
            "Iteration-62: Best Tour Length = 166.0\n",
            "Iteration-63: Best Tour Length = 166.0\n",
            "Iteration-64: Best Tour Length = 166.0\n",
            "Iteration-65: Best Tour Length = 166.0\n",
            "Iteration-66: Best Tour Length = 166.0\n",
            "Iteration-67: Best Tour Length = 166.0\n",
            "Iteration-68: Best Tour Length = 166.0\n",
            "Iteration-69: Best Tour Length = 166.0\n",
            "Iteration-70: Best Tour Length = 166.0\n",
            "Iteration-71: Best Tour Length = 178.0\n",
            "Iteration-72: Best Tour Length = 178.0\n",
            "Iteration-73: Best Tour Length = 166.0\n",
            "Iteration-74: Best Tour Length = 206.0\n",
            "Iteration-75: Best Tour Length = 166.0\n",
            "Iteration-76: Best Tour Length = 166.0\n",
            "Iteration-77: Best Tour Length = 166.0\n",
            "Iteration-78: Best Tour Length = 166.0\n",
            "Iteration-79: Best Tour Length = 166.0\n",
            "Iteration-80: Best Tour Length = 166.0\n",
            "Iteration-81: Best Tour Length = 166.0\n",
            "Iteration-82: Best Tour Length = 166.0\n",
            "Iteration-83: Best Tour Length = 166.0\n",
            "Iteration-84: Best Tour Length = 166.0\n",
            "Iteration-85: Best Tour Length = 192.0\n",
            "Iteration-86: Best Tour Length = 192.0\n",
            "Iteration-87: Best Tour Length = 166.0\n",
            "Iteration-88: Best Tour Length = 166.0\n",
            "Iteration-89: Best Tour Length = 166.0\n",
            "Iteration-90: Best Tour Length = 178.0\n",
            "Iteration-91: Best Tour Length = 166.0\n",
            "Iteration-92: Best Tour Length = 166.0\n",
            "Iteration-93: Best Tour Length = 166.0\n",
            "Iteration-94: Best Tour Length = 166.0\n",
            "Iteration-95: Best Tour Length = 166.0\n",
            "Iteration-96: Best Tour Length = 166.0\n",
            "Iteration-97: Best Tour Length = 166.0\n",
            "Iteration-98: Best Tour Length = 166.0\n",
            "Iteration-99: Best Tour Length = 166.0\n",
            "Iteration-100: Best Tour Length = 206.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def generate_initial_circuit(num_ants, num_gates, num_connections):\n",
        "    circuits = []\n",
        "    for _ in range(num_ants):\n",
        "        circuit = np.random.randint(2, size=(num_gates, num_connections))\n",
        "        circuits.append(circuit)\n",
        "    return circuits\n",
        "def evaluate_fitness(circuit):\n",
        "    return np.sum(circuit)\n",
        "def local_search(circuit):\n",
        "    row_to_flip = np.random.randint(circuit.shape[0])\n",
        "    circuit[row_to_flip] = 1 - circuit[row_to_flip]\n",
        "    return circuit\n",
        "def ant_colony_optimization(n_ants, n_iterations, rho, tau0, alpha, beta, Q, num_gates, num_connections):\n",
        "    pheromones = np.full((num_gates, num_connections), tau0)\n",
        "    best_circuit = None\n",
        "    best_fitness = float('-inf')\n",
        "    for iteration in range(n_iterations):\n",
        "        ants = generate_initial_circuit(n_ants, num_gates, num_connections)\n",
        "        for ant in ants:\n",
        "            probabilities = pheromones ** alpha * (1 / (evaluate_fitness(ant) + 1)) ** beta\n",
        "            probabilities /= probabilities.sum(axis=1)[:, np.newaxis]\n",
        "            for i in range(num_gates):\n",
        "                chosen_connection = np.random.choice(num_connections, p=probabilities[i])\n",
        "                ant[i, :] = 0\n",
        "                ant[i, chosen_connection] = 1\n",
        "            ant = local_search(ant)\n",
        "            fitness = evaluate_fitness(ant)\n",
        "            delta_tau = Q / fitness\n",
        "            pheromones = (1 - rho) * pheromones + delta_tau\n",
        "        for ant in ants:\n",
        "            ant_fitness = evaluate_fitness(ant)\n",
        "            if ant_fitness > best_fitness:\n",
        "                best_circuit = ant.copy()\n",
        "                best_fitness = ant_fitness\n",
        "        pheromones = (1 - rho) * pheromones\n",
        "        delta_tau_best = Q / best_fitness\n",
        "        pheromones = pheromones + delta_tau_best\n",
        "    return best_circuit\n",
        "n_ants = 10\n",
        "n_iterations = 100\n",
        "rho = 0.1\n",
        "tau0 = 1.0\n",
        "alpha = 1.0\n",
        "beta = 2.0\n",
        "Q = 1.0\n",
        "num_gates = 5\n",
        "num_connections = 10\n",
        "best_solution = ant_colony_optimization(n_ants, n_iterations, rho, tau0, alpha, beta, Q, num_gates, num_connections)\n",
        "print(\"Best Circuit =\")\n",
        "print(best_solution)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKj1pHRXRQrj",
        "outputId": "76577020-f095-4be2-9047-a4816371fafa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Circuit =\n",
            "[[1 1 1 1 1 1 0 1 1 1]\n",
            " [0 0 0 0 0 1 0 0 0 0]\n",
            " [0 1 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 1 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 1 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def generate_initial_circuit(num_ants, num_gates, num_connections):\n",
        "    circuits = []\n",
        "    for _ in range(num_ants):\n",
        "        circuit = np.random.randint(2, size=(num_gates, num_connections))\n",
        "        circuits.append(circuit)\n",
        "    return circuits\n",
        "def evaluate_fitness(circuit):\n",
        "    return np.sum(circuit)\n",
        "def local_search(circuit):\n",
        "    row_to_flip = np.random.randint(circuit.shape[0])\n",
        "    circuit[row_to_flip] = 1 - circuit[row_to_flip]\n",
        "    return circuit\n",
        "def ant_colony_optimization(n_ants, n_iterations, rho, tau0, alpha, beta, Q, num_gates, max_connections):\n",
        "    pheromones = np.full((num_gates, max_connections), tau0)\n",
        "    best_circuit = None\n",
        "    best_fitness = float('-inf')\n",
        "    for iteration in range(n_iterations):\n",
        "        ants = generate_initial_circuit(n_ants, num_gates, max_connections)\n",
        "        for ant in ants:\n",
        "            probabilities = pheromones ** alpha * (1 / (evaluate_fitness(ant) + 1)) ** beta\n",
        "            probabilities /= probabilities.sum(axis=1)[:, np.newaxis]\n",
        "            for i in range(num_gates):\n",
        "                chosen_connection = np.random.choice(max_connections, p=probabilities[i])\n",
        "                ant[i, :] = 0\n",
        "                ant[i, chosen_connection] = 1\n",
        "            ant = local_search(ant)\n",
        "            fitness = evaluate_fitness(ant)\n",
        "            delta_tau = Q / fitness\n",
        "            pheromones = (1 - rho) * pheromones + delta_tau\n",
        "            unused_connections = np.where(np.sum(ant, axis=0) == 0)[0]\n",
        "            pheromones[:, unused_connections] += 0.1\n",
        "        for ant in ants:\n",
        "            ant_fitness = evaluate_fitness(ant)\n",
        "            if ant_fitness > best_fitness:\n",
        "                best_circuit = ant.copy()\n",
        "                best_fitness = ant_fitness\n",
        "        pheromones = (1 - rho) * pheromones\n",
        "        delta_tau_best = Q / best_fitness\n",
        "        pheromones = pheromones + delta_tau_best\n",
        "    return best_circuit\n",
        "n_ants = 10\n",
        "n_iterations = 100\n",
        "rho = 0.1\n",
        "tau0 = 1.0\n",
        "alpha = 1.0\n",
        "beta = 2.0\n",
        "Q = 1.0\n",
        "num_gates = 5\n",
        "max_connections = 10\n",
        "best_solution = ant_colony_optimization(n_ants, n_iterations, rho, tau0, alpha, beta, Q, num_gates, max_connections)\n",
        "print(\"Best Circuit =\")\n",
        "print(best_solution)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfZo8VBTRjeW",
        "outputId": "7e644c0a-388e-495c-a80b-7b9693759ffe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Circuit =\n",
            "[[1 1 1 1 1 0 1 1 1 1]\n",
            " [0 1 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 1 0 0]\n",
            " [0 0 0 0 0 0 0 1 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "MAX_ITERATIONS = 100\n",
        "NUM_ANTS = 10\n",
        "PHEROMONE_INITIAL = 0.1\n",
        "ALPHA = 1.0\n",
        "BETA = 1.0\n",
        "Q = 1.0\n",
        "EVAPORATION_RATE = 0.1\n",
        "def initialize_graph(num_qubits):\n",
        "    return np.zeros((num_qubits, num_qubits), dtype=int)\n",
        "def initialize_pheromones(graph, initial_pheromone):\n",
        "    return np.full_like(graph, initial_pheromone, dtype=float)\n",
        "def construct_solution(graph, pheromones, alpha, beta):\n",
        "    num_qubits = graph.shape[0]\n",
        "    solution = []\n",
        "    current_qubit = 0\n",
        "    while current_qubit < num_qubits - 1:\n",
        "        next_qubit = choose_next_qubit(graph, pheromones, current_qubit, alpha, beta)\n",
        "        solution.append((current_qubit, next_qubit))\n",
        "        current_qubit = next_qubit\n",
        "    return solution\n",
        "def choose_next_qubit(graph, pheromones, current_qubit, alpha, beta):\n",
        "    probabilities = calculate_probabilities(graph, pheromones, current_qubit, alpha, beta)\n",
        "    return np.random.choice(np.arange(len(probabilities)), p=probabilities)\n",
        "def calculate_probabilities(graph, pheromones, current_qubit, alpha, beta):\n",
        "    edge_weights = graph[current_qubit]\n",
        "    pheromone_weights = pheromones[current_qubit]\n",
        "    probabilities = (pheromone_weights ** alpha) * (edge_weights ** beta)\n",
        "    if np.sum(probabilities) == 0:\n",
        "        return np.ones_like(probabilities) / len(probabilities)\n",
        "    else:\n",
        "        probabilities /= np.sum(probabilities)\n",
        "        return probabilities\n",
        "def calculate_quantum_cost(solution):\n",
        "    num_toffoli_gates = sum([1 for gate in solution if gate[1] != gate[0] + 1])\n",
        "    num_cnot_gates = len(solution) - num_toffoli_gates\n",
        "    quantum_cost = num_toffoli_gates + 2 * num_cnot_gates\n",
        "    return quantum_cost\n",
        "def calculate_garbage_outputs(solution):\n",
        "    return len([gate for gate in solution if gate[1] != gate[0] + 1])\n",
        "def update_pheromones(pheromones, solution, energy_consumption, Q):\n",
        "    evaporation = EVAPORATION_RATE\n",
        "    pheromones *= (1 - evaporation)\n",
        "    for gate in solution:\n",
        "        pheromones[gate] += Q / energy_consumption\n",
        "def visualize_circuit(solution, num_qubits):\n",
        "    circuit_matrix = np.zeros((num_qubits, len(solution)), dtype=int)\n",
        "    for i, (control, target) in enumerate(solution):\n",
        "        circuit_matrix[control, i] = 1\n",
        "        circuit_matrix[target, i] = 1\n",
        "    print(\"Reversible Circuit =\")\n",
        "    print(circuit_matrix)\n",
        "num_qubits = 4\n",
        "graph = initialize_graph(num_qubits)\n",
        "pheromones = initialize_pheromones(graph, PHEROMONE_INITIAL)\n",
        "best_solution = None\n",
        "best_energy_consumption = float('inf')\n",
        "pheromone_history = np.zeros((MAX_ITERATIONS, num_qubits, num_qubits))\n",
        "for iteration in range(MAX_ITERATIONS):\n",
        "    ant_solutions = []\n",
        "    for ant in range(NUM_ANTS):\n",
        "        solution = construct_solution(graph, pheromones, ALPHA, BETA)\n",
        "        ant_solutions.append(solution)\n",
        "    for ant_solution in ant_solutions:\n",
        "        qc = calculate_quantum_cost(ant_solution)\n",
        "        garbage_outputs = calculate_garbage_outputs(ant_solution)\n",
        "        energy_consumption = qc + garbage_outputs\n",
        "        update_pheromones(pheromones, ant_solution, energy_consumption, Q)\n",
        "        if energy_consumption < best_energy_consumption:\n",
        "            best_solution = ant_solution\n",
        "            best_energy_consumption = energy_consumption\n",
        "    pheromone_history[iteration] = pheromones.copy()\n",
        "visualize_circuit(best_solution, num_qubits)\n",
        "def plot_pheromone_evolution(pheromone_history):\n",
        "    iterations = range(len(pheromone_history))\n",
        "    pheromone_mean = np.mean(pheromone_history, axis=(1, 2))\n",
        "    plt.plot(iterations, pheromone_mean, label='Average Pheromone Level')\n",
        "    plt.xlabel('Iteration')\n",
        "    plt.ylabel('Pheromone Level')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "plot_pheromone_evolution(pheromone_history)\n",
        "initial_solution = construct_solution(graph, pheromones, ALPHA, BETA)\n",
        "initial_qc = calculate_quantum_cost(initial_solution)\n",
        "initial_garbage_outputs = calculate_garbage_outputs(initial_solution)\n",
        "initial_energy_consumption = initial_qc + initial_garbage_outputs\n",
        "print(\"Initial Energy Consumption =\", initial_energy_consumption)\n",
        "print(\"Optimized Energy Consumption =\", best_energy_consumption)\n",
        "print(\"Efficiency Improvement =\", (initial_energy_consumption - best_energy_consumption) / initial_energy_consumption * 100, \"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "--9vX_KjVN0u",
        "outputId": "5d1db2e3-e271-488e-a110-940cf5c89435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reversible Circuit =\n",
            "[[1]\n",
            " [0]\n",
            " [0]\n",
            " [1]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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8W567YsUKDBgwAH5+fvDz84NGo2lwfmlpKcaPH4/Q0FB4enpi6NChOHbsmK0f466I6wB5sAWIiIhICrImQJmZmdBqtUhJScH+/fvRo0cPxMXFoays7Kbn5+TkYPTo0di+fTtyc3OhVqsxZMgQnDlzBgAgCAJGjBiBEydO4IsvvsCBAwfQtm1baDQaVFVVSfloFtGxBYiIiEhSCkEQBLluHhsbi5iYGCxZsgQAYDKZoFarMX36dMyZM+eO1xuNRvj5+WHJkiUYO3YsCgoK0LlzZ/zyyy/o2rWrWGZwcDDeeustPP/8842KS6fTQaVSoaKiAj4+Pk1/wEbq8ca3qKgxYJt2IDq09Lb5/YiIiJojS+pv2VqA9Ho99u3bB41Gcz0YJydoNBrk5uY2qozq6moYDAb4+/sDAGprawEA7u7uZmUqlUrs3LnzluXU1tZCp9OZfaQiCMINY4DYBUZERCQF2RKg8vJyGI1GBAUFmR0PCgpCSUlJo8qYPXs2QkNDxSQqPDwcbdq0QVJSEv744w/o9Xqkp6fj9OnTOHfu3C3LSU1NhUqlEj9qtbrpD2ahKr0RpmttcJwGT0REJA3ZB0E3VVpaGjZs2IBNmzaJLT6urq7YuHEjCgoK4O/vD09PT2zfvh2PPPIInJxu/ahJSUmoqKgQP8XFxVI9htj64+KkgLvrPfvrICIiuqfINuo2ICAAzs7OKC0tNTteWlqK4ODg2167aNEipKWlYdu2bejevbvZd1FRUTh48CAqKiqg1+sRGBiI2NhYREdH37I8pVIJpVLZ9Ie5CzeuAq1QKGSJgYiIyNHI1uTg5uaGqKgoZGdni8dMJhOys7PRp0+fW163cOFCzJ8/H1lZWbdNalQqFQIDA3Hs2DHs3bsXw4cPt2r81nJ9J3h2fxEREUlF1nnXWq0W48aNQ3R0NHr37o2MjAxUVVUhMTERADB27Fi0atUKqampAID09HQkJydj/fr1CAsLE8cKeXl5wcvLCwDw6aefIjAwEG3atMHPP/+MGTNmYMSIERgyZIg8D3kHuhruA0ZERCQ1WWvd+Ph4nD9/HsnJySgpKUFkZCSysrLEgdFFRUVmY3eWLVsGvV6PkSNHmpWTkpKCefPmAQDOnTsHrVaL0tJShISEYOzYsZg7d65kz2QpcQ0gJVuAiIiIpCLrOkD2Ssp1gD7+6RT+7/NfMCQiCP8ae+suPSIiIrq9e2IdILpKxzFAREREkmMCJLMbZ4ERERGRNBpV62q12kYX+M477zQ5GEfEVaCJiIik16gE6MCBA40qjOvYWK5+FpgPW4CIiIgk06had/v27baOw2GJ6wCxBYiIiEgyTR4D9Pvvv2PLli2oqakBcHVTT7IcxwARERFJz+IE6MKFCxg8eDA6deqERx99VNxkdMKECXjppZesHmBzdz0BYgsQERGRVCxOgGbNmgVXV1cUFRXB09NTPB4fH4+srCyrBucIrk+DZwsQERGRVCyudb/99lts2bIFrVu3NjvesWNHnDp1ymqBOYoagxEA4OnmLHMkREREjsPiFqCqqiqzlp96Fy9elG1H9XuZoc4EAHB15pJMREREUrG41h0wYADWrFkj/qxQKGAymbBw4UL8+c9/tmpwjsBgujp43IUJEBERkWQs7gJbuHAhBg8ejL1790Kv1+PVV1/Fr7/+iosXL2LXrl22iLFZqzNeawFy4hpKREREUrG42eGBBx5AQUEB+vfvj+HDh6OqqgpPPvkkDhw4gPbt29sixmbLaBJwrQGILUBEREQSatLUI5VKhddff93asTgcw7XWHwBwdWYLEBERkVQsbnbo0KED5s2bh2PHjtkiHodSZ7q+eCQHQRMREUnH4lp36tSp2Lx5Mzp37oyYmBi8++67KCkpsUVszV79DDAAcOEYICIiIsk0aSHEPXv24LfffsOjjz6KpUuXQq1WY8iQIWazw+jODKarCZBCATgzASIiIpJMk/tdOnXqhDfeeAMFBQX44YcfcP78eSQmJloztmavzni1C8zVyQkKBRMgIiIiqdzV/gt5eXlYv349MjMzodPp8PTTT1srLodQPwjahQOgiYiIJGVxAlRQUIB169bhk08+QWFhIR566CGkp6fjySefhJeXly1ibLYM9S1AHABNREQkKYsToPDwcMTExGDq1KkYNWoUgoKCbBGXQ6gz1W+DwRYgIiIiKVmcAB09ehQdO3a0RSwOx1B3bRsMJ7YAERERScnimrdjx464dOkSVq5ciaSkJFy8eBEAsH//fpw5c8bqATZn9bPAXF3YAkRERCQli1uADh8+jMGDB8PX1xcnT57ExIkT4e/vj40bN6KoqIhT4S1w4ywwIiIikk6T1gFKTEzEsWPH4O7uLh5/9NFH8f3331s1uOaOs8CIiIjkYXEL0N69e/Gvf/2rwfFWrVpxRWgL1SdAnAVGREQkLYtrXqVSCZ1O1+B4QUEBAgMDrRKUo6jvAuNO8ERERNKyuOZ9/PHH8be//Q0GgwEAoFAoUFRUhNmzZ+Opp56yeoDNmTgNnttgEBERScriBOjtt99GZWUlWrZsiZqaGgwaNAgdOnSAl5cXFixYYIsYmy09F0IkIiKShcVjgFQqFbZu3Ypdu3bh0KFDqKysRK9evaDRaGwRX7NWx0HQREREsmjyXmD9+vVDv379xJ9/++03PP744ygoKLBKYI6gji1AREREsrBazVtbW4vjx49bqziHoDdyKwwiIiI5sOlBRte7wPhrICIikhJrXhnVmepXgmYLEBERkZSYAMlIz4UQiYiIZNHomtfPzw/+/v63/AwYMKBJASxduhRhYWFwd3dHbGws8vLybnnuihUrMGDAAPj5+cHPzw8ajabB+ZWVlZg2bRpat24NDw8PREREYPny5U2Kzda4ECIREZE8Gj0LLCMjw+o3z8zMhFarxfLlyxEbG4uMjAzExcXh6NGjaNmyZYPzc3JyMHr0aPTt2xfu7u5IT0/HkCFD8Ouvv6JVq1YAAK1Wi++++w4ff/wxwsLC8O2332LKlCkIDQ3F448/bvVnuBt1HARNREQkC4UgCIJcN4+NjUVMTAyWLFkCADCZTFCr1Zg+fTrmzJlzx+uNRiP8/PywZMkSjB07FgDwwAMPID4+HnPnzhXPi4qKwiOPPII333zzpuXU1taitrZW/Fmn00GtVqOiogI+Pj5384i3lfbNb1i+4zgm9G+HuX+JsNl9iIiIHIFOp4NKpWpU/S1b34ter8e+ffvMFlB0cnKCRqNBbm5uo8qorq6GwWCAv7+/eKxv37748ssvcebMGQiCgO3bt6OgoABDhgy5ZTmpqalQqVTiR61WN/3BLMCFEImIiOQhWwJUXl4Oo9GIoKAgs+NBQUGN3lV+9uzZCA0NNUuiFi9ejIiICLRu3Rpubm4YOnQoli5dioEDB96ynKSkJFRUVIif4uLipj2Uha7PAuMYICIiIik1eSVouaWlpWHDhg3IycmBu7u7eHzx4sX46aef8OWXX6Jt27b4/vvvMXXq1AaJ0o2USiWUSqVUoYs4C4yIiEgesiVAAQEBcHZ2Rmlpqdnx0tJSBAcH3/baRYsWIS0tDdu2bUP37t3F4zU1NXjttdewadMmDBs2DADQvXt3HDx4EIsWLbK7/crYBUZERCSPJjc96PV6HD16FHV1dU263s3NDVFRUcjOzhaPmUwmZGdno0+fPre8buHChZg/fz6ysrIQHR1t9p3BYIDBYIDT/3QpOTs7w2QyNSlOW7q+FxgTICIiIilZnABVV1djwoQJ8PT0RNeuXVFUVAQAmD59OtLS0iwqS6vVYsWKFfjoo4+Qn5+PF154AVVVVUhMTAQAjB07FklJSeL56enpmDt3LlatWoWwsDCUlJSgpKQElZWVAAAfHx8MGjQIr7zyCnJyclBYWIjVq1djzZo1eOKJJyx9VJtjFxgREZE8LK55k5KScOjQoQZjbzQaDTIzMy0qKz4+HosWLUJycjIiIyNx8OBBZGVliQOji4qKcO7cOfH8ZcuWQa/XY+TIkQgJCRE/ixYtEs/ZsGEDYmJikJCQgIiICKSlpWHBggWYPHmypY9qc1wIkYiISB4WjwH6/PPPkZmZiT/96U9QKK533XTt2rVJu8FPmzYN06ZNu+l3OTk5Zj+fPHnyjuUFBwfjww8/tDgOOdRd65bjXmBERETSsrjp4fz58zddpbmqqsosIaI704tjgNgCREREJCWLa97o6Ghs3rxZ/Lk+6Vm5cuVtBy9TQ5wFRkREJA+Lu8DeeustPPLIIzhy5Ajq6urw7rvv4siRI/jxxx+xY8cOW8TYbNWxBYiIiEgWFte8/fv3x8GDB1FXV4du3brh22+/RcuWLZGbm4uoqChbxNhscRYYERGRPJq0EGL79u2xYsUKa8ficOoHQbMLjIiISFpNSoBMJhN+//13lJWVNVhg8HZ7bpE5sQuMe4ERERFJyuIE6KeffsJf//pXnDp1CoIgmH2nUChgNBqtFlxzd70LjC1AREREUrI4AZo8ebI4EywkJIRT3+8CF0IkIiKSh8UJ0LFjx/Cf//wHHTp0sEU8DqWOLUBERESysLjpITY2Fr///rstYnE4XAiRiIhIHha3AE2fPh0vvfQSSkpK0K1bN7i6upp93717d6sF19yJW2GwBYiIiEhSFidATz31FADgueeeE48pFAoIgsBB0BYSxwBxFhgREZGkLE6ACgsLbRGHQ9JzKwwiIiJZWJwAtW3b1hZxOKT6QdBuHANEREQkqSYthHj8+HFkZGQgPz8fABAREYEZM2agffv2Vg2uOTOaBJiuLaPEafBERETSsrjm3bJlCyIiIpCXl4fu3buje/fu2L17N7p27YqtW7faIsZmyWC8voI2u8CIiIikZXEL0Jw5czBr1iykpaU1OD579mw8/PDDVguuOaszXV9Fm11gRERE0rK45s3Pz8eECRMaHH/uuedw5MgRqwTlCOpubAFyYgsQERGRlCxOgAIDA3Hw4MEGxw8ePIiWLVtaIyaHoL8hAXJmAkRERCQpi7vAJk6ciEmTJuHEiRPo27cvAGDXrl1IT0+HVqu1eoDNVf0aQG7OTtxPjYiISGIWJ0Bz586Ft7c33n77bSQlJQEAQkNDMW/ePLz44otWD7C5ur4RKpMfIiIiqVmcACkUCsyaNQuzZs3C5cuXAQDe3t5WD6y5M1zbBoPjf4iIiKTXpHWA6jHxabr6afBuLpwBRkREJDWLE6ALFy4gOTkZ27dvR1lZGUwmk9n3Fy9etFpwzRn3ASMiIpKPxQnQmDFj8Pvvv2PChAkICgriAN4mMnAfMCIiItlYnAD98MMP2LlzJ3r06GGLeByG4YZZYERERCQti2vf8PBw1NTU2CIWh1LHFiAiIiLZWJwAvf/++3j99dexY8cOXLhwATqdzuxDjWMwcQwQERGRXCzuAvP19YVOp8NDDz1kdlwQBCgUChiNRqsF15wZ6q62ALlyFhgREZHkLE6AEhIS4OrqivXr13MQ9F2ouzZ7zpXrABEREUnO4gTol19+wYEDB9C5c2dbxOMwDFwJmoiISDYW979ER0ejuLjYFrE4lPpp8K6cBUZERCQ5i1uApk+fjhkzZuCVV15Bt27d4OrqavZ99+7drRZcc1a/ECITICIiIulZnADFx8cDAJ577jnxmEKh4CBoC3EvMCIiIvlY3PxQWFjY4HPixAnxf5ti6dKlCAsLg7u7O2JjY5GXl3fLc1esWIEBAwbAz88Pfn5+0Gg0Dc5XKBQ3/fz9739vUny2wFlgRERE8rG4Baht27ZWDSAzMxNarRbLly9HbGwsMjIyEBcXh6NHj6Jly5YNzs/JycHo0aPRt29fuLu7Iz09HUOGDMGvv/6KVq1aAQDOnTtnds0333yDCRMm4KmnnrJq7Hej7to6QJwFRkREJD2FIAiCpRcdP34cGRkZyM/PBwBERERgxowZaN++vcUBxMbGIiYmBkuWLAEAmEwmqNVqTJ8+HXPmzLnj9UajEX5+fliyZAnGjh1703NGjBiBy5cvIzs7u1Ex6XQ6qFQqVFRUwMfHp/EPY4FlOceRnvUbRka1xqKnua0IERHR3bKk/ra4/2XLli2IiIhAXl4eunfvju7du2P37t3o2rUrtm7dalFZer0e+/btg0ajuR6QkxM0Gg1yc3MbVUZ1dTUMBgP8/f1v+n1paSk2b96MCRMm3LKM2tpayVe05iwwIiIi+VjcBTZnzhzMmjULaWlpDY7Pnj0bDz/8cKPLKi8vh9FoRFBQkNnxoKAg/Pbbb40qY/bs2QgNDTVLom700UcfwdvbG08++eQty0hNTcUbb7zR6LitoU5MgNgFRkREJDWLmx/y8/Nv2pry3HPP4ciRI1YJqrHS0tKwYcMGbNq0Ce7u7jc9Z9WqVUhISLjl9wCQlJSEiooK8SPFOkfcC4yIiEg+FrcABQYG4uDBg+jYsaPZ8YMHD9500PLtBAQEwNnZGaWlpWbHS0tLERwcfNtrFy1ahLS0NGzbtu2Waw/98MMPOHr0KDIzM29bllKphFKptCj2u3V9FhhbgIiIiKRmcQI0ceJETJo0CSdOnEDfvn0BALt27UJ6ejq0Wq1FZbm5uSEqKgrZ2dkYMWIEgKuDoLOzszFt2rRbXrdw4UIsWLAAW7ZsQXR09C3P++CDDxAVFYUePexvkPH1WWBsASIiIpKaxQnQ3Llz4e3tjbfffhtJSUkAgNDQUMybNw8vvviixQFotVqMGzcO0dHR6N27NzIyMlBVVYXExEQAwNixY9GqVSukpqYCANLT05GcnIz169cjLCwMJSUlAAAvLy94eXmJ5ep0Onz66ad4++23LY5JCvWDoLkXGBERkfQsSoDq6uqwfv16/PWvf8WsWbNw+fJlAIC3t3eTA4iPj8f58+eRnJyMkpISREZGIisrSxwYXVRUBKcbWkmWLVsGvV6PkSNHmpWTkpKCefPmiT9v2LABgiBg9OjRTY7NljgLjIiISD4WrwPk6emJ/Px8qy+IaE+kWAdIm3kQGw+cwWuPhmPSQMvXTyIiIiJzNl0HqHfv3jhw4ECTg6OrOAuMiIhIPhaPAZoyZQpeeuklnD59GlFRUWjRooXZ99wNvnG4FxgREZF8LE6ARo0aBQBmA565G7zl6q7tBs+9wIiIiKRncQJUWFhoizgcjsF4rQuMg6CJiIgkJ/tu8I7KwK0wiIiIZNOk5oe1a9eiX79+CA0NxalTpwAAGRkZ+OKLL6waXHNWd60FiNPgiYiIpGdx7bts2TJotVo8+uijuHTpkjjmx9fXFxkZGdaOr9kyXBsD5MIxQERERJKzOAFavHgxVqxYgddffx3Ozs7i8ejoaPz8889WDa45E7vAOAuMiIhIchbXvoWFhejZs2eD40qlElVVVVYJyhGIXWBcB4iIiEhyFte+7dq1w8GDBxscz8rKQpcuXawRk0PgXmBERETysXgWmFarxdSpU3HlyhUIgoC8vDx88sknSE1NxcqVK20RY7Nk4CBoIiIi2VicAD3//PPw8PDA//3f/6G6uhp//etfERoainfffVdcJJHurI7T4ImIiGRjcQIEAAkJCUhISEB1dTUqKyvRsmVLa8fV7HEvMCIiIvk0KQGq5+npCU9PT2vF4lC4ECIREZF8LG5+KC0txZgxYxAaGgoXFxc4OzubfahxuBAiERGRfCxuARo/fjyKioowd+5chISEQKFgC0ZTcBYYERGRfCxOgHbu3IkffvgBkZGRNgjHcdSZ2AJEREQkF4trX7VaDUEQbBGLwzCZBBiZABEREcnG4to3IyMDc+bMwcmTJ20QjmOo3wcMYBcYERGRHBrVBebn52c21qeqqgrt27eHp6cnXF1dzc69ePGidSNshuoHQAPcCoOIiEgOjUqAuMu7ddUPgAY4DZ6IiEgOjUqAxo0bZ+s4HIrhhhYgZycmQERERFJrdP+LyWRCeno6+vXrh5iYGMyZMwc1NTW2jK3ZqjNdXwSRywgQERFJr9EJ0IIFC/Daa6/By8sLrVq1wrvvvoupU6faMrZmy1DHGWBERERyanQNvGbNGrz//vvYsmULPv/8c3z11VdYt24dTDfMaKLGqZ8F5sLuLyIiIlk0OgEqKirCo48+Kv6s0WigUChw9uxZmwTWnHEbDCIiInk1ugauq6uDu7u72TFXV1cYDAarB9XcXd8IlQkQERGRHBq9FYYgCBg/fjyUSqV47MqVK5g8eTJatGghHtu4caN1I2yGuA8YERGRvBqdAN1sKvyzzz5r1WAcBfcBIyIiklejE6APP/zQlnE4FEPd9WnwREREJD02QcjAcK0FyIXbYBAREcmCNbAM6oxsASIiIpITEyAZcBYYERGRvFgDy6B+LzDOAiMiIpKH7AnQ0qVLERYWBnd3d8TGxiIvL++W565YsQIDBgyAn58f/Pz8oNFobnp+fn4+Hn/8cahUKrRo0QIxMTEoKiqy5WNY5PpeYLK/fiIiIockaw2cmZkJrVaLlJQU7N+/Hz169EBcXBzKyspuen5OTg5Gjx6N7du3Izc3F2q1GkOGDMGZM2fEc44fP47+/fsjPDwcOTk5OHz4MObOndtgEUc5cS8wIiIieSkEQRDkunlsbCxiYmKwZMkSAFd3nFer1Zg+fTrmzJlzx+uNRiP8/PywZMkSjB07FgAwatQouLq6Yu3atU2OS6fTQaVSoaKiAj4+Pk0u51bW7T6F1zf9giERQfjX2Girl09EROSILKm/ZWuC0Ov12LdvHzQazfVgnJyg0WiQm5vbqDKqq6thMBjg7+8P4GoCtXnzZnTq1AlxcXFo2bIlYmNj8fnnn9+2nNraWuh0OrOPLXEvMCIiInnJVgOXl5fDaDQiKCjI7HhQUBBKSkoaVcbs2bMRGhoqJlFlZWWorKxEWloahg4dim+//RZPPPEEnnzySezYseOW5aSmpkKlUokftVrd9AdrBAOnwRMREcmq0StB25u0tDRs2LABOTk54vge07XBxcOHD8esWbMAAJGRkfjxxx+xfPlyDBo06KZlJSUlQavVij/rdDqbJkHXZ4GxBYiIiEgOsiVAAQEBcHZ2Rmlpqdnx0tJSBAcH3/baRYsWIS0tDdu2bUP37t3NynRxcUFERITZ+V26dMHOnTtvWZ5SqTTb5NXWuBAiERGRvGRrgnBzc0NUVBSys7PFYyaTCdnZ2ejTp88tr1u4cCHmz5+PrKwsREebDyB2c3NDTEwMjh49ana8oKAAbdu2te4D3AUuhEhERCQvWbvAtFotxo0bh+joaPTu3RsZGRmoqqpCYmIiAGDs2LFo1aoVUlNTAQDp6elITk7G+vXrERYWJo4V8vLygpeXFwDglVdeQXx8PAYOHIg///nPyMrKwldffYWcnBxZnvFmuBcYERGRvGRNgOLj43H+/HkkJyejpKQEkZGRyMrKEgdGFxUVwemGJGHZsmXQ6/UYOXKkWTkpKSmYN28eAOCJJ57A8uXLkZqaihdffBGdO3fGZ599hv79+0v2XHfCLjAiIiJ5yboOkL2y9TpA8778Fat/PIlpf+6Al+M6W718IiIiR3RPrAPkyOrHAHEvMCIiInkwAZIBF0IkIiKSF2tgGXAhRCIiInkxAZIBZ4ERERHJizWwDDgLjIiISF5MgGTAhRCJiIjkxRpYBtwLjIiISF6sgWVQZ2IXGBERkZyYAMnAUMdp8ERERHJiDSwDw7UWIBcntgARERHJgQmQDLgQIhERkbxYA8uAs8CIiIjkxRpYBtwLjIiISF5MgGRQZ6rvAmMCREREJAcmQDKoHwPErTCIiIjkwRpYBnqOASIiIpIVa2AZcC8wIiIieTEBkkEdt8IgIiKSFWtgGejZAkRERCQrJkAyuD4LjK+fiIhIDqyBJWYyCTCa6meBsQWIiIhIDkyAJFa/DxgAuLrw9RMREcmBNbDE6gdAA4Ar1wEiIiKSBWtgid2YAHErDCIiInkwAZJY/QwwgGOAiIiI5MIESGJ1putT4BUKJkBERERyYAIkMe4DRkREJD/WwhLjIohERETyYwIksfoWIC6CSEREJB/WwhIzXGsB4gwwIiIi+TABkphB7ALjqyciIpILa2GJcR8wIiIi+bEWlpjYBcY1gIiIiGTDBEhiBg6CJiIikh1rYYnVcRo8ERGR7OwiAVq6dCnCwsLg7u6O2NhY5OXl3fLcFStWYMCAAfDz84Ofnx80Gk2D88ePHw+FQmH2GTp0qK0fo1HqW4Bc2AJEREQkG9lr4czMTGi1WqSkpGD//v3o0aMH4uLiUFZWdtPzc3JyMHr0aGzfvh25ublQq9UYMmQIzpw5Y3be0KFDce7cOfHzySefSPE4d2RgCxAREZHsZE+A3nnnHUycOBGJiYmIiIjA8uXL4enpiVWrVt30/HXr1mHKlCmIjIxEeHg4Vq5cCZPJhOzsbLPzlEolgoODxY+fn98tY6itrYVOpzP72Mr1vcBkf/VEREQOS9ZaWK/XY9++fdBoNOIxJycnaDQa5ObmNqqM6upqGAwG+Pv7mx3PyclBy5Yt0blzZ7zwwgu4cOHCLctITU2FSqUSP2q1umkP1AhiFxhngREREclG1gSovLwcRqMRQUFBZseDgoJQUlLSqDJmz56N0NBQsyRq6NChWLNmDbKzs5Geno4dO3bgkUcegdFovGkZSUlJqKioED/FxcVNf6g74EKIRERE8nORO4C7kZaWhg0bNiAnJwfu7u7i8VGjRol/7tatG7p374727dsjJycHgwcPblCOUqmEUqmUJGbuBUZERCQ/WWvhgIAAODs7o7S01Ox4aWkpgoODb3vtokWLkJaWhm+//Rbdu3e/7bn3338/AgIC8Pvvv991zHeLe4ERERHJT9YEyM3NDVFRUWYDmOsHNPfp0+eW1y1cuBDz589HVlYWoqOj73if06dP48KFCwgJCbFK3HeDCyESERHJT/ZaWKvVYsWKFfjoo4+Qn5+PF154AVVVVUhMTAQAjB07FklJSeL56enpmDt3LlatWoWwsDCUlJSgpKQElZWVAIDKykq88sor+Omnn3Dy5ElkZ2dj+PDh6NChA+Li4mR5xhtxIUQiIiL5yT4GKD4+HufPn0dycjJKSkoQGRmJrKwscWB0UVERnJyu52nLli2DXq/HyJEjzcpJSUnBvHnz4OzsjMOHD+Ojjz7CpUuXEBoaiiFDhmD+/PmSjfO5HYOpfhaY7LknERGRw5I9AQKAadOmYdq0aTf9Licnx+znkydP3rYsDw8PbNmyxUqRWR9ngREREcmPtbDE2AVGREQkPyZAEru+FxgTICIiIrkwAZIYu8CIiIjkx1pYYlwIkYiISH6shSVmuLYZKvcCIyIikg8TIImxBYiIiEh+rIUlZuAsMCIiItkxAZLY9VlgfPVERERyYS0ssToTZ4ERERHJjbWwxNgFRkREJD8mQBITu8C4FxgREZFsWAtLrH4rDK4ETUREJB8mQBKrbwFy4xggIiIi2bAWlpiBLUBERESyYwIksToTxwARERHJjbWwxOpbgNxc2AJEREQkFyZAEqvjLDAiIiLZsRaWGMcAERERyY8JkMTELjDOAiMiIpINa2GJ1XEvMCIiItmxFpaY4dpeYC5O7AIjIiKSCxMgiYkLIbrw1RMREcmFtbCEBEGAUVwHiC1AREREcmECJKH61h+AY4CIiIjkxFpYQvUzwADOAiMiIpITa2EJ1Zm1ALELjIiISC5MgCRUPwMM4BggIiIiOTEBklB9F5irswIKBRMgIiIiuTABkhD3ASMiIrIPLnIH4Ei4DxjRvcdoNMJgMMgdBhEBcHZ2houLi1V6UZgASUhcBJEzwIjuCZWVlTh9+jQEQbjzyUQkCU9PT4SEhMDNze2uymECJCG2ABHdO4xGI06fPg1PT08EBgZy3B6RzARBgF6vx/nz51FYWIiOHTvC6S6GlDABklCdiWOAiO4VBoMBgiAgMDAQHh4ecodDRAA8PDzg6uqKU6dOQa/Xw93dvcllsSaWUH0LEPcBI7p3sOWHyL7cTauPWTlWKeUuLV26FGFhYXB3d0dsbCzy8vJuee6KFSswYMAA+Pn5wc/PDxqN5rbnT548GQqFAhkZGTaI3DJiFxjXACIiIpKV7AlQZmYmtFotUlJSsH//fvTo0QNxcXEoKyu76fk5OTkYPXo0tm/fjtzcXKjVagwZMgRnzpxpcO6mTZvw008/ITQ01NaP0SjiNHgOgiYiIpKV7DXxO++8g4kTJyIxMRERERFYvnw5PD09sWrVqpuev27dOkyZMgWRkZEIDw/HypUrYTKZkJ2dbXbemTNnMH36dKxbtw6urq5SPModiV1gHARNRCSpBx98EDNnzpQ7DGqEkydPQqFQ4ODBgza9j6wJkF6vx759+6DRaMRjTk5O0Gg0yM3NbVQZ1dXVMBgM8Pf3F4+ZTCaMGTMGr7zyCrp27XrHMmpra6HT6cw+tmBgCxARSSQ3NxfOzs4YNmyY3KFIQqFQiB+VSoV+/frhu+++kzuse5KjJIuy1sTl5eUwGo0ICgoyOx4UFISSkpJGlTF79myEhoaaJVHp6elwcXHBiy++2KgyUlNToVKpxI9arW78Q1igzsQxQEQkjQ8++ADTp0/H999/j7Nnz9r0XoIgoK6uzqb3aIwPP/wQ586dw65duxAQEIC//OUvOHHihM3up9frbVY22d493RSRlpaGDRs2YNOmTeJUuH379uHdd9/F6tWrGz17IykpCRUVFeKnuLjYJvFyFhjRvUsQBFTr62T5WLoQY2VlJTIzM/HCCy9g2LBhWL16tfjdX//6V8THx5udbzAYEBAQgDVr1gC42oqempqKdu3awcPDAz169MB//vMf8fycnBwoFAp88803iIqKglKpxM6dO3H8+HEMHz4cQUFB8PLyQkxMDLZt22Z2r3PnzmHYsGHw8PBAu3btsH79eoSFhZlNVLl06RKef/55BAYGwsfHBw899BAOHTp0x+f29fVFcHAwHnjgASxbtgw1NTXYunWr+L3JZMKrr74Kf39/BAcHY968eWbX3+m+8+bNQ2RkJFauXIl27dqJ9U5RURGGDx8OLy8v+Pj44JlnnkFpaWmD61atWoU2bdrAy8sLU6ZMgdFoxMKFCxEcHIyWLVtiwYIFZvE0tty1a9ciLCwMKpUKo0aNwuXLl82e+Xa/y6bYuXMnBgwYAA8PD6jVarz44ouoqqoCALz22muIjY1tcE2PHj3wt7/9Tfx55cqV6NKlC9zd3REeHo7333//rmJqClnXAQoICICzs7PZLxQASktLERwcfNtrFy1ahLS0NGzbtg3du3cXj//www8oKytDmzZtxGNGoxEvvfQSMjIycPLkyQZlKZVKKJXKu3uYRhC7wNgCRHTPqTEYEZG8RZZ7H/lbHDzdGv/P9b///W+Eh4ejc+fOePbZZzFz5kwkJSVBoVAgISEBTz/9NCorK+Hl5QUA2LJlC6qrq/HEE08AuNoq/vHHH2P58uXo2LEjvv/+ezz77LMIDAzEoEGDxPvMmTMHixYtwv333w8/Pz8UFxfj0UcfxYIFC6BUKrFmzRo89thjOHr0qPhv8tixY1FeXo6cnBy4urpCq9U2mPTy9NNPw8PDA9988w1UKhX++c9/YvDgwSgoKDAb7nA79Ws33dhK89FHH0Gr1WL37t3Izc3F+PHj0a9fPzz88MONvu/vv/+Ozz77DBs3boSzszNMJpOYpOzYsQN1dXWYOnUq4uPjkZOTI977+PHj+Oabb5CVlYXjx49j5MiROHHiBDp16oQdO3bgxx9/xHPPPQeNRoPY2FiLyv3888/x9ddf448//sAzzzyDtLQ0MZlq7O+ysY4fP46hQ4fizTffxKpVq3D+/HlMmzYN06ZNw4cffoiEhASkpqbi+PHjaN++PQDg119/xeHDh/HZZ58BuDqWNzk5GUuWLEHPnj1x4MABTJw4ES1atMC4ceMsjqnJBJn17t1bmDZtmviz0WgUWrVqJaSmpt7ymvT0dMHHx0fIzc1t8F15ebnw888/m31CQ0OF2bNnC7/99lujYqqoqBAACBUVFZY/0G2s++mU0Hb218LzH+2xarlEZH01NTXCkSNHhJqaGkEQBKGq1iC0nf21LJ+qWoNFsfft21fIyMgQBEEQDAaDEBAQIGzfvt3s5zVr1ojnjx49WoiPjxcEQRCuXLkieHp6Cj/++KNZmRMmTBBGjx4tCIIgbN++XQAgfP7553eMpWvXrsLixYsFQRCE/Px8AYCwZ8/1fwOPHTsmABD+8Y9/CIIgCD/88IPg4+MjXLlyxayc9u3bC//85z9veR8AwqZNmwRBEISqqiphypQpgrOzs3Do0CFBEARh0KBBQv/+/c2uiYmJEWbPnt3o+6akpAiurq5CWVmZ+P23334rODs7C0VFReKxX3/9VQAg5OXlidd5enoKOp1OPCcuLk4ICwsTjEajeKxz585i3dfUcl955RUhNjZWEITG/S5vZtCgQcKMGTNu+t2ECROESZMmmR374YcfBCcnJ/HvSo8ePYS//e1v4vdJSUliTIJw9Z2uX7/erIz58+cLffr0EQRBEAoLCwUAwoEDB24aw//+3byRJfW37CtBa7VajBs3DtHR0ejduzcyMjJQVVWFxMREAFf/30KrVq2QmpoK4Or4nuTkZLHZtH6skJeXF7y8vHDffffhvvvuM7uHq6srgoOD0blzZ2kf7n9cnwXGLjCie42HqzOO/C1Otns31tGjR5GXl4dNmzYBAFxcXBAfH48PPvgADz74IFxcXPDMM89g3bp1GDNmDKqqqvDFF19gw4YNAK62cFRXV4utIvX0ej169uxpdiw6Otrs58rKSsybNw+bN2/GuXPnUFdXh5qaGhQVFYmxubi4oFevXuI1HTp0gJ+fn/jzoUOHUFlZ2eDf8ZqaGhw/fvy2zz569Gg4OzujpqYGgYGB+OCDD8x6CG78MwCEhISIrU+NvW/btm0RGBgo/pyfnw+1Wm02djQiIgK+vr7Iz89HTEwMACAsLAze3t7iOUFBQXB2djZb1C8oKEiMp6nl3vhMlvwuG+vQoUM4fPgw1q1bJx4TBAEmkwmFhYXo0qULEhISsGrVKsydOxeCIOCTTz6BVqsFAFRVVeH48eOYMGECJk6cKJZRV1cHlUrVpJiaSvYEKD4+HufPn0dycjJKSkoQGRmJrKwscWB0UVGR2X8gy5Ytg16vx8iRI83KSUlJadCfa2+4FxjRvUuhUFjUDSWXDz74AHV1dWbrnwmCAKVSiSVLlkClUiEhIQGDBg1CWVkZtm7dCg8PDwwdOhTA1SQGADZv3oxWrVqZlf2/QwVatGhh9vPLL7+MrVu3YtGiRejQoQM8PDwwcuRIiwYLV1ZWIiQkxKybp56vr+9tr/3HP/4BjUYDlUpllqTU+98lURQKBUzXJqc09r7/+8yNdbN73y6euyn3xmcCGve7bKzKykr8v//3/246yai+m3P06NGYPXs29u/fj5qaGhQXF4vjzupjWrFiRYOxQs7OjU/0rcEu/jbX9x/ezP/+x3izMTx30pRrbIF7gRGRLdXV1WHNmjV4++23MWTIELPvRowYgU8++QSTJ09G3759oVarkZmZiW+++QZPP/20WJFGRERAqVSiqKjI4jEiu3btwvjx48WxRJWVlWb//nbu3Bl1dXU4cOAAoqKiAFxtpfjjjz/Ec3r16oWSkhK4uLggLCzMovsHBwejQ4cOFl1zt/ft0qULiouLUVxcLLbWHDlyBJcuXUJERESTYrFWuXfzu7yVXr164ciRI7d9z61bt8agQYOwbt061NTU4OGHH0bLli0BXG3lCg0NxYkTJ5CQkGCVmJrKLhIgR+GkANxdneDuygSIiKyvfiDshAkTGnQnPPXUU/jggw8wefJkAFdngy1fvhwFBQXYvn27eJ63tzdefvllzJo1CyaTCf3790dFRQV27doFHx+f2w5S7dixIzZu3IjHHnsMCoUCc+fONWvRCA8Ph0ajwaRJk7Bs2TK4urripZdegoeHhzhrV6PRoE+fPhgxYgQWLlyITp064ezZs9i8eTOeeOKJBt1u1tLU+2o0GnTr1g0JCQnIyMhAXV0dpkyZgkGDBt1VrNYo925+l+fPn2+wEGFISAhmz56NP/3pT5g2bRqef/55tGjRAkeOHMHWrVuxZMkS8dyEhASkpKRAr9fjH//4h1k5b7zxBl588UWoVCoMHToUtbW12Lt3L/744w+xq0wKrIklNGlge/w2/xEseKKb3KEQUTP0wQcfiF1A/+upp57C3r17cfjwYQBXK6gjR46gVatW6Nevn9m58+fPx9y5c5GamoouXbpg6NCh2Lx5M9q1a3fb+7/zzjvw8/ND37598dhjjyEuLs5svA8ArFmzBkFBQRg4cCCeeOIJTJw4Ed7e3uKUcoVCgf/+978YOHAgEhMT0alTJ4waNQqnTp1qsGacNTX1vgqFAl988QX8/PwwcOBAaDQa3H///cjMzLzreKxRblN/l+vXr0fPnj3NPitWrED37t2xY8cOFBQUYMCAAejZsyeSk5MbbDk1cuRIXLhwAdXV1RgxYoTZd88//zxWrlyJDz/8EN26dcOgQYOwevXqO8ZkbQpBsHCBCQeg0+mgUqlQUVEBHx8fucMhIhlcuXIFhYWFZuu9kPWdPn0aarUa27Ztw+DBg+UOh+4Bt/u7aUn9zS4wIiKSzHfffYfKykp069YN586dw6uvvoqwsDAMHDhQ7tDIwTABIiIiyRgMBrz22ms4ceIEvL290bdvX7vatJocBxMgIiKSTFxcHOLi5FlPiehGHARNREREDocJEBHRbXCeCJF9sdbfSSZAREQ3Ub8qrSWrGBOR7VVXVwNouAq2pTgGiIjoJlxcXODp6Ynz58/D1dXVbEseIpKeIAiorq5GWVkZfH1973rrDCZAREQ3oVAoEBISgsLCQpw6dUrucIjoGl9fXwQHB991OUyAiIhuwc3NDR07dmQ3GJGdcHV1tdqmqUyAiIhuw8nJiStBEzVD7NQmIiIih8MEiIiIiBwOEyAiIiJyOBwDdBP1iyzpdDqZIyEiIqLGqq+3G7NYIhOgm7h8+TIAQK1WyxwJERERWery5ctQqVS3PUchcJ33BkwmE86ePQtvb28oFAqrlq3T6aBWq1FcXAwfHx+rlk3m+K6lw3ctHb5r6fBdS8da71oQBFy+fBmhoaF3XLyULUA34eTkhNatW9v0Hj4+PvwLJRG+a+nwXUuH71o6fNfSsca7vlPLTz0OgiYiIiKHwwSIiIiIHA4TIIkplUqkpKRAqVTKHUqzx3ctHb5r6fBdS4fvWjpyvGsOgiYiIiKHwxYgIiIicjhMgIiIiMjhMAEiIiIih8MEiIiIiBwOEyAJLV26FGFhYXB3d0dsbCzy8vLkDumel5qaipiYGHh7e6Nly5YYMWIEjh49anbOlStXMHXqVNx3333w8vLCU089hdLSUpkibj7S0tKgUCgwc+ZM8RjftfWcOXMGzz77LO677z54eHigW7du2Lt3r/i9IAhITk5GSEgIPDw8oNFocOzYMRkjvjcZjUbMnTsX7dq1g4eHB9q3b4/58+eb7SXFd90033//PR577DGEhoZCoVDg888/N/u+Me/14sWLSEhIgI+PD3x9fTFhwgRUVlZaJT4mQBLJzMyEVqtFSkoK9u/fjx49eiAuLg5lZWVyh3ZP27FjB6ZOnYqffvoJW7duhcFgwJAhQ1BVVSWeM2vWLHz11Vf49NNPsWPHDpw9exZPPvmkjFHf+/bs2YN//vOf6N69u9lxvmvr+OOPP9CvXz+4urrim2++wZEjR/D222/Dz89PPGfhwoV47733sHz5cuzevRstWrRAXFwcrly5ImPk95709HQsW7YMS5YsQX5+PtLT07Fw4UIsXrxYPIfvummqqqrQo0cPLF269KbfN+a9JiQk4Ndff8XWrVvx9ddf4/vvv8ekSZOsE6BAkujdu7cwdepU8Wej0SiEhoYKqampMkbV/JSVlQkAhB07dgiCIAiXLl0SXF1dhU8//VQ8Jz8/XwAg5ObmyhXmPe3y5ctCx44dha1btwqDBg0SZsyYIQgC37U1zZ49W+jfv/8tvzeZTEJwcLDw97//XTx26dIlQalUCp988okUITYbw4YNE5577jmzY08++aSQkJAgCALftbUAEDZt2iT+3Jj3euTIEQGAsGfPHvGcb775RlAoFMKZM2fuOia2AElAr9dj37590Gg04jEnJydoNBrk5ubKGFnzU1FRAQDw9/cHAOzbtw8Gg8Hs3YeHh6NNmzZ89000depUDBs2zOydAnzX1vTll18iOjoaTz/9NFq2bImePXtixYoV4veFhYUoKSkxe9cqlQqxsbF81xbq27cvsrOzUVBQAAA4dOgQdu7ciUceeQQA37WtNOa95ubmwtfXF9HR0eI5Go0GTk5O2L17913HwM1QJVBeXg6j0YigoCCz40FBQfjtt99kiqr5MZlMmDlzJvr164cHHngAAFBSUgI3Nzf4+vqanRsUFISSkhIZory3bdiwAfv378eePXsafMd3bT0nTpzAsmXLoNVq8dprr2HPnj148cUX4ebmhnHjxonv82b/pvBdW2bOnDnQ6XQIDw+Hs7MzjEYjFixYgISEBADgu7aRxrzXkpIStGzZ0ux7FxcX+Pv7W+XdMwGiZmPq1Kn45ZdfsHPnTrlDaZaKi4sxY8YMbN26Fe7u7nKH06yZTCZER0fjrbfeAgD07NkTv/zyC5YvX45x48bJHF3z8u9//xvr1q3D+vXr0bVrVxw8eBAzZ85EaGgo33Uzxy4wCQQEBMDZ2bnBbJjS0lIEBwfLFFXzMm3aNHz99dfYvn07WrduLR4PDg6GXq/HpUuXzM7nu7fcvn37UFZWhl69esHFxQUuLi7YsWMH3nvvPbi4uCAoKIjv2kpCQkIQERFhdqxLly4oKioCAPF98t+Uu/fKK69gzpw5GDVqFLp164YxY8Zg1qxZSE1NBcB3bSuNea/BwcENJgrV1dXh4sWLVnn3TIAk4ObmhqioKGRnZ4vHTCYTsrOz0adPHxkju/cJgoBp06Zh06ZN+O6779CuXTuz76OiouDq6mr27o8ePYqioiK+ewsNHjwYP//8Mw4ePCh+oqOjkZCQIP6Z79o6+vXr12A5h4KCArRt2xYA0K5dOwQHB5u9a51Oh927d/NdW6i6uhpOTuZVobOzM0wmEwC+a1tpzHvt06cPLl26hH379onnfPfddzCZTIiNjb37IO56GDU1yoYNGwSlUimsXr1aOHLkiDBp0iTB19dXKCkpkTu0e9oLL7wgqFQqIScnRzh37pz4qa6uFs+ZPHmy0KZNG+G7774T9u7dK/Tp00fo06ePjFE3HzfOAhMEvmtrycvLE1xcXIQFCxYIx44dE9atWyd4enoKH3/8sXhOWlqa4OvrK3zxxRfC4cOHheHDhwvt2rUTampqZIz83jNu3DihVatWwtdffy0UFhYKGzduFAICAoRXX31VPIfvumkuX74sHDhwQDhw4IAAQHjnnXeEAwcOCKdOnRIEoXHvdejQoULPnj2F3bt3Czt37hQ6duwojB492irxMQGS0OLFi4U2bdoIbm5uQu/evYWffvpJ7pDueQBu+vnwww/Fc2pqaoQpU6YIfn5+gqenp/DEE08I586dky/oZuR/EyC+a+v56quvhAceeEBQKpVCeHi48K9//cvse5PJJMydO1cICgoSlEqlMHjwYOHo0aMyRXvv0ul0wowZM4Q2bdoI7u7uwv333y+8/vrrQm1trXgO33XTbN++/ab/Po8bN04QhMa91wsXLgijR48WvLy8BB8fHyExMVG4fPmyVeJTCMINy10SEREROQCOASIiIiKHwwSIiIiIHA4TICIiInI4TICIiIjI4TABIiIiIofDBIiIiIgcDhMgIiIicjhMgIiIiMjhMAEiIrqJsLAwZGRkyB0GEdkIEyAikt348eMxYsQIAMCDDz6ImTNnSnbv1atXw9fXt8HxPXv2YNKkSZLFQUTScpE7ACIiW9Dr9XBzc2vy9YGBgVaMhojsDVuAiMhujB8/Hjt27MC7774LhUIBhUKBkydPAgB++eUXPPLII/Dy8kJQUBDGjBmD8vJy8doHH3wQ06ZNw8yZMxEQEIC4uDgAwDvvvINu3bqhRYsWUKvVmDJlCiorKwEAOTk5SExMREVFhXi/efPmAWjYBVZUVIThw4fDy8sLPj4+eOaZZ1BaWip+P2/ePERGRmLt2rUICwuDSqXCqFGjcPnyZdu+NCJqEiZARGQ33n33XfTp0wcTJ07EuXPncO7cOajValy6dAkPPfQQevbsib179yIrKwulpaV45plnzK7/6KOP4Obmhl27dmH58uUAACcnJ7z33nv49ddf8dFHH+G7777Dq6++CgDo27cvMjIy4OPjI97v5ZdfbhCXyWTC8OHDcfHiRezYsQNbt27FiRMnEB8fb3be8ePH8fnnn+Prr7/G119/jR07diAtLc1Gb4uI7ga7wIjIbqhUKri5ucHT0xPBwcHi8SVLlqBnz5546623xGOrVq2CWq1GQUEBOnXqBADo2LEjFi5caFbmjeOJwsLC8Oabb2Ly5Ml4//334ebmBpVKBYVCYXa//5WdnY2ff/4ZhYWFUKvVAIA1a9aga9eu2LNnD2JiYgBcTZRWr14Nb29vAMCYMWOQnZ2NBQsW3N2LISKrYwsQEdm9Q4cOYfv27fDy8hI/4eHhAK62utSLiopqcO22bdswePBgtGrVCt7e3hgzZgwuXLiA6urqRt8/Pz8farVaTH4AICIiAr6+vsjPzxePhYWFickPAISEhKCsrMyiZyUiabAFiIjsXmVlJR577DGkp6c3+C4kJET8c4sWLcy+O3nyJP7yl7/ghRdewIIFC+Dv74+dO3diwoQJ0Ov18PT0tGqcrq6uZj8rFAqYTCar3oOIrIMJEBHZFTc3NxiNRrNjvXr1wmeffYawsDC4uDT+n619+/bBZDLh7bffhpPT1Qbvf//733e83//q0qULiouLUVxcLLYCHTlyBJcuXUJERESj4yEi+8EuMCKyK2FhYdi9ezdOnjyJ8vJymEwmTJ06FRcvXsTo0aOxZ88eHD9+HFu2bEFiYuJtk5cOHTrAYDBg8eLFOHHiBNauXSsOjr7xfpWVlcjOzkZ5eflNu8Y0Gg26deuGhIQE7N+/H3l5eRg7diwGDRqE6Ohoq78DIrI9JkBEZFdefvllODs7IyIiAoGBgSgqKkJoaCh27doFo9GIIUOGoFu3bpg5cyZ8fX3Flp2b6dGjB9555x2kp6fjgQcewLp165Cammp2Tt++fTF58mTEx8cjMDCwwSBq4GpX1hdffAE/Pz8MHDgQGo0G999/PzIzM63+/EQkDYUgCILcQRARERFJiS1ARERE5HCYABEREZHDYQJEREREDocJEBERETkcJkBERETkcJgAERERkcNhAkREREQOhwkQERERORwmQERERORwmAARERGRw2ECRERERA7n/wO1VBoy1rzGoQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Energy Consumption = 12\n",
            "Optimized Energy Consumption = 2\n",
            "Efficiency Improvement = 83.33333333333334 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install libgraphviz-dev\n",
        "!pip install pygraphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fx9Rj4tTV1jt",
        "outputId": "be3363fa-07ea-4330-9c11-4be0597dcc35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libgail-common libgail18 libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libgvc6-plugins-gtk\n",
            "  librsvg2-common libxdot4\n",
            "Suggested packages:\n",
            "  gvfs\n",
            "The following NEW packages will be installed:\n",
            "  libgail-common libgail18 libgraphviz-dev libgtk2.0-0 libgtk2.0-bin libgtk2.0-common\n",
            "  libgvc6-plugins-gtk librsvg2-common libxdot4\n",
            "0 upgraded, 9 newly installed, 0 to remove and 33 not upgraded.\n",
            "Need to get 2,433 kB of archives.\n",
            "After this operation, 7,694 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-common all 2.24.33-2ubuntu2 [125 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-0 amd64 2.24.33-2ubuntu2 [2,037 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail18 amd64 2.24.33-2ubuntu2 [15.9 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail-common amd64 2.24.33-2ubuntu2 [132 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libxdot4 amd64 2.42.2-6 [16.4 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgvc6-plugins-gtk amd64 2.42.2-6 [22.6 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgraphviz-dev amd64 2.42.2-6 [58.5 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-bin amd64 2.24.33-2ubuntu2 [7,932 B]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 librsvg2-common amd64 2.52.5+dfsg-3ubuntu0.2 [17.7 kB]\n",
            "Fetched 2,433 kB in 1s (2,993 kB/s)\n",
            "Selecting previously unselected package libgtk2.0-common.\n",
            "(Reading database ... 121749 files and directories currently installed.)\n",
            "Preparing to unpack .../0-libgtk2.0-common_2.24.33-2ubuntu2_all.deb ...\n",
            "Unpacking libgtk2.0-common (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libgtk2.0-0:amd64.\n",
            "Preparing to unpack .../1-libgtk2.0-0_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgtk2.0-0:amd64 (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libgail18:amd64.\n",
            "Preparing to unpack .../2-libgail18_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgail18:amd64 (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libgail-common:amd64.\n",
            "Preparing to unpack .../3-libgail-common_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgail-common:amd64 (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libxdot4:amd64.\n",
            "Preparing to unpack .../4-libxdot4_2.42.2-6_amd64.deb ...\n",
            "Unpacking libxdot4:amd64 (2.42.2-6) ...\n",
            "Selecting previously unselected package libgvc6-plugins-gtk.\n",
            "Preparing to unpack .../5-libgvc6-plugins-gtk_2.42.2-6_amd64.deb ...\n",
            "Unpacking libgvc6-plugins-gtk (2.42.2-6) ...\n",
            "Selecting previously unselected package libgraphviz-dev:amd64.\n",
            "Preparing to unpack .../6-libgraphviz-dev_2.42.2-6_amd64.deb ...\n",
            "Unpacking libgraphviz-dev:amd64 (2.42.2-6) ...\n",
            "Selecting previously unselected package libgtk2.0-bin.\n",
            "Preparing to unpack .../7-libgtk2.0-bin_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgtk2.0-bin (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package librsvg2-common:amd64.\n",
            "Preparing to unpack .../8-librsvg2-common_2.52.5+dfsg-3ubuntu0.2_amd64.deb ...\n",
            "Unpacking librsvg2-common:amd64 (2.52.5+dfsg-3ubuntu0.2) ...\n",
            "Setting up libxdot4:amd64 (2.42.2-6) ...\n",
            "Setting up librsvg2-common:amd64 (2.52.5+dfsg-3ubuntu0.2) ...\n",
            "Setting up libgtk2.0-common (2.24.33-2ubuntu2) ...\n",
            "Setting up libgtk2.0-0:amd64 (2.24.33-2ubuntu2) ...\n",
            "Setting up libgvc6-plugins-gtk (2.42.2-6) ...\n",
            "Setting up libgail18:amd64 (2.24.33-2ubuntu2) ...\n",
            "Setting up libgtk2.0-bin (2.24.33-2ubuntu2) ...\n",
            "Setting up libgail-common:amd64 (2.24.33-2ubuntu2) ...\n",
            "Setting up libgraphviz-dev:amd64 (2.42.2-6) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libgdk-pixbuf-2.0-0:amd64 (2.42.8+dfsg-1ubuntu0.2) ...\n",
            "Collecting pygraphviz\n",
            "  Downloading pygraphviz-1.12.tar.gz (104 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.9/104.9 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pygraphviz\n",
            "  Building wheel for pygraphviz (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pygraphviz: filename=pygraphviz-1.12-cp310-cp310-linux_x86_64.whl size=168137 sha256=d1541077340d60bc8ecfee638bbed90b6b5d56f943046fc74e9486037f28ad78\n",
            "  Stored in directory: /root/.cache/pip/wheels/1d/ee/b5/a2f54f9e9b3951599c05dcce270ca85e472f8e6cec470e397a\n",
            "Successfully built pygraphviz\n",
            "Installing collected packages: pygraphviz\n",
            "Successfully installed pygraphviz-1.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pygraphviz as pgv\n",
        "import os\n",
        "from IPython.display import Image\n",
        "MAX_ITERATIONS = 100\n",
        "NUM_ANTS = 10\n",
        "PHEROMONE_INITIAL = 0.1\n",
        "ALPHA = 1.0\n",
        "BETA = 1.0\n",
        "Q = 1.0\n",
        "EVAPORATION_RATE = 0.1\n",
        "def initialize_graph(num_qubits):\n",
        "    return np.zeros((num_qubits, num_qubits), dtype=int)\n",
        "def initialize_pheromones(graph, initial_pheromone):\n",
        "    return np.full_like(graph, initial_pheromone, dtype=float)\n",
        "def construct_solution(graph, pheromones, alpha, beta):\n",
        "    num_qubits = graph.shape[0]\n",
        "    solution = []\n",
        "    current_qubit = 0\n",
        "    while current_qubit < num_qubits - 1:\n",
        "        next_qubit = choose_next_qubit(graph, pheromones, current_qubit, alpha, beta)\n",
        "        solution.append((current_qubit, next_qubit))\n",
        "        current_qubit = next_qubit\n",
        "    return solution\n",
        "def choose_next_qubit(graph, pheromones, current_qubit, alpha, beta):\n",
        "    probabilities = calculate_probabilities(graph, pheromones, current_qubit, alpha, beta)\n",
        "    return np.random.choice(np.arange(len(probabilities)), p=probabilities)\n",
        "def calculate_probabilities(graph, pheromones, current_qubit, alpha, beta):\n",
        "    edge_weights = graph[current_qubit]\n",
        "    pheromone_weights = pheromones[current_qubit]\n",
        "    probabilities = (pheromone_weights ** alpha) * (edge_weights ** beta)\n",
        "    if np.sum(probabilities) == 0:\n",
        "        return np.ones_like(probabilities) / len(probabilities)\n",
        "    else:\n",
        "        probabilities /= np.sum(probabilities)\n",
        "        return probabilities\n",
        "def calculate_quantum_cost(solution):\n",
        "    num_toffoli_gates = sum([1 for gate in solution if gate[1] != gate[0] + 1])\n",
        "    num_cnot_gates = len(solution) - num_toffoli_gates\n",
        "    quantum_cost = num_toffoli_gates + 2 * num_cnot_gates\n",
        "    return quantum_cost\n",
        "def calculate_garbage_outputs(solution):\n",
        "    return len([gate for gate in solution if gate[1] != gate[0] + 1])\n",
        "def update_pheromones(pheromones, solution, energy_consumption, Q):\n",
        "    evaporation = EVAPORATION_RATE\n",
        "    pheromones *= (1 - evaporation)\n",
        "    for gate in solution:\n",
        "        pheromones[gate] += Q / energy_consumption\n",
        "def visualize_circuit(solution, num_qubits):\n",
        "    circuit_matrix = np.zeros((num_qubits, len(solution)), dtype=int)\n",
        "    for i, (control, target) in enumerate(solution):\n",
        "        circuit_matrix[control, i] = 1\n",
        "        circuit_matrix[target, i] = 1\n",
        "    print(\"Reversible Circuit =\")\n",
        "    print(circuit_matrix)\n",
        "    graph = pgv.AGraph(directed=True)\n",
        "    for qubit in range(num_qubits):\n",
        "        graph.add_node(qubit, shape='circle')\n",
        "    for i, (control, target) in enumerate(solution):\n",
        "        graph.add_edge(control, target, label=f'Gate {i + 1}\\n{control}->{target}')\n",
        "    graph_file_path = 'reversible_circuit_graph.png'\n",
        "    graph.draw(graph_file_path, format='png', prog='dot')\n",
        "    current_directory = os.getcwd()\n",
        "    image_path = os.path.join(current_directory, 'reversible_circuit_graph.png')\n",
        "    Image(filename=image_path)\n",
        "num_qubits = 4\n",
        "graph = initialize_graph(num_qubits)\n",
        "pheromones = initialize_pheromones(graph, PHEROMONE_INITIAL)\n",
        "best_solution = None\n",
        "best_energy_consumption = float('inf')\n",
        "pheromone_history = np.zeros((MAX_ITERATIONS, num_qubits, num_qubits))\n",
        "for iteration in range(MAX_ITERATIONS):\n",
        "    ant_solutions = []\n",
        "    for ant in range(NUM_ANTS):\n",
        "        solution = construct_solution(graph, pheromones, ALPHA, BETA)\n",
        "        ant_solutions.append(solution)\n",
        "    for ant_solution in ant_solutions:\n",
        "        qc = calculate_quantum_cost(ant_solution)\n",
        "        garbage_outputs = calculate_garbage_outputs(ant_solution)\n",
        "        energy_consumption = qc + garbage_outputs\n",
        "        update_pheromones(pheromones, ant_solution, energy_consumption, Q)\n",
        "        if energy_consumption < best_energy_consumption:\n",
        "            best_solution = ant_solution\n",
        "            best_energy_consumption = energy_consumption\n",
        "    pheromone_history[iteration] = pheromones.copy()\n",
        "visualize_circuit(best_solution, num_qubits)\n",
        "def plot_pheromone_evolution(pheromone_history):\n",
        "    iterations = range(len(pheromone_history))\n",
        "    pheromone_mean = np.mean(pheromone_history, axis=(1, 2))\n",
        "plot_pheromone_evolution(pheromone_history)\n",
        "initial_solution = construct_solution(graph, pheromones, ALPHA, BETA)\n",
        "initial_qc = calculate_quantum_cost(initial_solution)\n",
        "initial_garbage_outputs = calculate_garbage_outputs(initial_solution)\n",
        "initial_energy_consumption = initial_qc + initial_garbage_outputs\n",
        "print(\"Initial Energy Consumption =\", initial_energy_consumption)\n",
        "print(\"Optimized Energy Consumption =\", best_energy_consumption)\n",
        "print(\"Efficiency Improvement =\", (initial_energy_consumption - best_energy_consumption) / initial_energy_consumption * 100, \"%\")\n",
        "current_directory = os.getcwd()\n",
        "image_path = os.path.join(current_directory, 'reversible_circuit_graph.png')\n",
        "Image(filename=image_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "OVQ5keVrXMp2",
        "outputId": "5b9d50a7-ab35-4d82-a321-01e02865fed8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reversible Circuit =\n",
            "[[1]\n",
            " [0]\n",
            " [0]\n",
            " [1]]\n",
            "Initial Energy Consumption = 14\n",
            "Optimized Energy Consumption = 2\n",
            "Efficiency Improvement = 85.71428571428571 %\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Antenna Optimization`**"
      ],
      "metadata": {
        "id": "E0TUqkgKY9f2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "num_ants = 10\n",
        "num_iterations = 100\n",
        "pheromone_decay = 0.5\n",
        "alpha = 1.0\n",
        "beta = 2.0\n",
        "grid_size = 10\n",
        "antenna_positions = np.random.rand(num_ants, 2) * grid_size\n",
        "pheromones = np.ones((grid_size, grid_size))\n",
        "loopback_vibrator = np.array([5, 5])\n",
        "unloopback_vibrator = np.array([8, 8])\n",
        "def calculate_heuristic(position):\n",
        "    distances = np.linalg.norm(antenna_positions - position[:, np.newaxis, :], axis=2)\n",
        "    return 1.0 / (distances + 1e-6)\n",
        "for iteration in range(num_iterations):\n",
        "    print(f\"\\nIteration {iteration + 1}:\")\n",
        "    for ant in range(num_ants):\n",
        "        current_position = antenna_positions[ant]\n",
        "        print(f\"\\nAnt {ant + 1} at Position: {current_position}\")\n",
        "        heuristic = calculate_heuristic(antenna_positions)\n",
        "        pheromone = pheromones.flatten()\n",
        "        probabilities = (pheromone.reshape((grid_size, grid_size)) ** alpha) * (heuristic ** beta)\n",
        "        current_positions_indices = np.ravel_multi_index(current_position.astype(int).T, (grid_size, grid_size))\n",
        "        probabilities.ravel()[current_positions_indices] = 0\n",
        "        selected_index = np.argmax(probabilities)\n",
        "        selected_position = np.unravel_index(selected_index, (grid_size, grid_size))\n",
        "        print(f\"Selected Next Position: {selected_position}\")\n",
        "        pheromones[selected_position] *= (1 - pheromone_decay)\n",
        "        antenna_positions[ant] = selected_position\n",
        "    print(\"\\nPheromone Matrix:\")\n",
        "    print(pheromones)\n",
        "print(\"\\nFinal Optimized Antenna Positions:\")\n",
        "print(antenna_positions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UiIIrl6OlVFi",
        "outputId": "ec81e92b-ef10-4ae7-a25b-31c2cf129cde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  9.53674316e-07 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 9.53674316e-07]]\n",
            "\n",
            "Iteration 23:\n",
            "\n",
            "Ant 1 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 2 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 3 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 4 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 5 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 6 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 7 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 8 at Position: [0. 0.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 9 at Position: [8. 7.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 10 at Position: [9. 9.]\n",
            "Selected Next Position: (9, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[4.76837158e-07 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 4.76837158e-07 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 4.76837158e-07 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 4.76837158e-07\n",
            "  5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  4.76837158e-07 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 4.76837158e-07 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 4.76837158e-07 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 4.76837158e-07\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  4.76837158e-07 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 4.76837158e-07]]\n",
            "\n",
            "Iteration 24:\n",
            "\n",
            "Ant 1 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 2 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 3 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 4 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 5 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 6 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 7 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 8 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 9 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 10 at Position: [9. 8.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.38418579e-07 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 2.38418579e-07 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 2.38418579e-07 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.38418579e-07\n",
            "  5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.38418579e-07 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.38418579e-07 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 2.38418579e-07 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 2.38418579e-07\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.38418579e-07 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.38418579e-07]]\n",
            "\n",
            "Iteration 25:\n",
            "\n",
            "Ant 1 at Position: [0. 0.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 2 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 1)\n",
            "\n",
            "Ant 3 at Position: [2. 2.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 4 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 5 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 6 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 7 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 8 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 9 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 10 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.19209290e-07 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 1.19209290e-07 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 1.19209290e-07 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 1.19209290e-07\n",
            "  5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.19209290e-07 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.19209290e-07 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.19209290e-07 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.19209290e-07\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.19209290e-07 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.38418579e-07]]\n",
            "\n",
            "Iteration 26:\n",
            "\n",
            "Ant 1 at Position: [1. 1.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 2 at Position: [0. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 3 at Position: [0. 0.]\n",
            "Selected Next Position: (1, 2)\n",
            "\n",
            "Ant 4 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 5 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 6 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 7 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 8 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 9 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 10 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[5.96046448e-08 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 5.96046448e-08 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.96046448e-08 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.96046448e-08\n",
            "  5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.96046448e-08 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 5.96046448e-08 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 5.96046448e-08 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 5.96046448e-08\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.19209290e-07 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.19209290e-07]]\n",
            "\n",
            "Iteration 27:\n",
            "\n",
            "Ant 1 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 2 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 3 at Position: [1. 2.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 4 at Position: [1. 1.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 5 at Position: [2. 2.]\n",
            "Selected Next Position: (3, 4)\n",
            "\n",
            "Ant 6 at Position: [3. 3.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 7 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 8 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 9 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 10 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.98023224e-08 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 2.98023224e-08 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 2.98023224e-08 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.98023224e-08\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.98023224e-08 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.98023224e-08 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 2.98023224e-08 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 5.96046448e-08\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.96046448e-08 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 5.96046448e-08]]\n",
            "\n",
            "Iteration 28:\n",
            "\n",
            "Ant 1 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 2 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 3 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 4 at Position: [2. 2.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 5 at Position: [3. 4.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 6 at Position: [1. 1.]\n",
            "Selected Next Position: (4, 5)\n",
            "\n",
            "Ant 7 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 8 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 9 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 10 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.49011612e-08 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 1.49011612e-08 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 1.49011612e-08 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 1.49011612e-08\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.49011612e-08 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.49011612e-08 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 2.98023224e-08 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 2.98023224e-08\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.98023224e-08 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.98023224e-08]]\n",
            "\n",
            "Iteration 29:\n",
            "\n",
            "Ant 1 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 2 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 3 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 4 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 5 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 6 at Position: [4. 5.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 7 at Position: [2. 2.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 8 at Position: [3. 3.]\n",
            "Selected Next Position: (6, 7)\n",
            "\n",
            "Ant 9 at Position: [4. 4.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 10 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[7.45058060e-09 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 7.45058060e-09 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 7.45058060e-09 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 7.45058060e-09\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  7.45058060e-09 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.49011612e-08 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.49011612e-08 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.49011612e-08\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.49011612e-08 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.49011612e-08]]\n",
            "\n",
            "Iteration 30:\n",
            "\n",
            "Ant 1 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 2 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 3 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 4 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 5 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 6 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 7 at Position: [3. 3.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 8 at Position: [6. 7.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 9 at Position: [2. 2.]\n",
            "Selected Next Position: (7, 8)\n",
            "\n",
            "Ant 10 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[3.7252903e-09 2.5000000e-01 1.0000000e+00 1.0000000e+00 1.0000000e+00\n",
            "  1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e-01]\n",
            " [5.0000000e-01 3.7252903e-09 2.5000000e-01 1.0000000e+00 1.0000000e+00\n",
            "  1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00]\n",
            " [1.0000000e+00 5.0000000e-01 3.7252903e-09 5.0000000e-01 1.0000000e+00\n",
            "  1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00]\n",
            " [1.0000000e+00 1.0000000e+00 5.0000000e-01 3.7252903e-09 2.5000000e-01\n",
            "  1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00]\n",
            " [1.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e-01 7.4505806e-09\n",
            "  2.5000000e-01 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00]\n",
            " [1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e-01\n",
            "  7.4505806e-09 5.0000000e-01 1.0000000e+00 1.0000000e+00 1.0000000e+00]\n",
            " [1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00\n",
            "  5.0000000e-01 7.4505806e-09 2.5000000e-01 1.0000000e+00 1.0000000e+00]\n",
            " [1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00\n",
            "  1.0000000e+00 5.0000000e-01 7.4505806e-09 2.5000000e-01 1.0000000e+00]\n",
            " [1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00\n",
            "  1.0000000e+00 1.0000000e+00 5.0000000e-01 7.4505806e-09 5.0000000e-01]\n",
            " [5.0000000e-01 1.0000000e+00 1.0000000e+00 1.0000000e+00 1.0000000e+00\n",
            "  1.0000000e+00 1.0000000e+00 1.0000000e+00 5.0000000e-01 7.4505806e-09]]\n",
            "\n",
            "Iteration 31:\n",
            "\n",
            "Ant 1 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 2 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 3 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 4 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 5 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 6 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 7 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 8 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 9 at Position: [7. 8.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 10 at Position: [3. 3.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.86264515e-09 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01]\n",
            " [5.00000000e-01 1.86264515e-09 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 1.86264515e-09 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 3.72529030e-09\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.86264515e-09 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 3.72529030e-09 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 3.72529030e-09 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 3.72529030e-09\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  3.72529030e-09 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 3.72529030e-09]]\n",
            "\n",
            "Iteration 32:\n",
            "\n",
            "Ant 1 at Position: [4. 4.]\n",
            "Selected Next Position: (0, 9)\n",
            "\n",
            "Ant 2 at Position: [5. 5.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 3 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 4 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 5 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 6 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 7 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 8 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 9 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 10 at Position: [4. 4.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[9.31322575e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [5.00000000e-01 9.31322575e-10 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 9.31322575e-10 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 1.86264515e-09\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.86264515e-09 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.86264515e-09 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.86264515e-09 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.86264515e-09\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.86264515e-09 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.86264515e-09]]\n",
            "\n",
            "Iteration 33:\n",
            "\n",
            "Ant 1 at Position: [0. 9.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 2 at Position: [3. 3.]\n",
            "Selected Next Position: (1, 0)\n",
            "\n",
            "Ant 3 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 4 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 5 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 6 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 7 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 8 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 9 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 10 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[4.65661287e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 4.65661287e-10 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 9.31322575e-10 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 9.31322575e-10\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  9.31322575e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 9.31322575e-10 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 9.31322575e-10 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 9.31322575e-10\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  9.31322575e-10 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 9.31322575e-10]]\n",
            "\n",
            "Iteration 34:\n",
            "\n",
            "Ant 1 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 2 at Position: [1. 0.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 3 at Position: [4. 4.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 4 at Position: [5. 5.]\n",
            "Selected Next Position: (2, 3)\n",
            "\n",
            "Ant 5 at Position: [6. 6.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 6 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 7 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 8 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 9 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 10 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.32830644e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 4.65661287e-10 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 4.65661287e-10 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 4.65661287e-10\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  4.65661287e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 4.65661287e-10 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 4.65661287e-10 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 4.65661287e-10\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  4.65661287e-10 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 4.65661287e-10]]\n",
            "\n",
            "Iteration 35:\n",
            "\n",
            "Ant 1 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 2 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 3 at Position: [5. 5.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 4 at Position: [2. 3.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 5 at Position: [4. 4.]\n",
            "Selected Next Position: (4, 3)\n",
            "\n",
            "Ant 6 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 7 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 8 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 9 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 10 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.32830644e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.32830644e-10 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 2.32830644e-10 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.32830644e-10\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  2.32830644e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.32830644e-10 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 2.32830644e-10 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 2.32830644e-10\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.32830644e-10 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.32830644e-10]]\n",
            "\n",
            "Iteration 36:\n",
            "\n",
            "Ant 1 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 2 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 3 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 4 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 5 at Position: [4. 3.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 6 at Position: [5. 5.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 7 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 6)\n",
            "\n",
            "Ant 8 at Position: [7. 7.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 9 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 10 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.16415322e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 1.16415322e-10 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 1.16415322e-10 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 1.16415322e-10\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  1.16415322e-10 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.16415322e-10 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.16415322e-10 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.16415322e-10\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.16415322e-10 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.32830644e-10]]\n",
            "\n",
            "Iteration 37:\n",
            "\n",
            "Ant 1 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 2 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 3 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 4 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 5 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 6 at Position: [6. 6.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 7 at Position: [5. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 8 at Position: [5. 5.]\n",
            "Selected Next Position: (7, 6)\n",
            "\n",
            "Ant 9 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 10 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[5.82076609e-11 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 5.82076609e-11 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.82076609e-11 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.82076609e-11\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  5.82076609e-11 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 5.82076609e-11 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 5.82076609e-11 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 5.82076609e-11\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.16415322e-10 5.00000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.16415322e-10]]\n",
            "\n",
            "Iteration 38:\n",
            "\n",
            "Ant 1 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 2 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 3 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 4 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 5 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 6 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 7 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 8 at Position: [7. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 9 at Position: [6. 6.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 10 at Position: [7. 7.]\n",
            "Selected Next Position: (8, 9)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.91038305e-11 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.91038305e-11 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 2.91038305e-11 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.91038305e-11\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  2.91038305e-11 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.91038305e-11 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 5.82076609e-11 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 2.91038305e-11\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.82076609e-11 2.50000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 5.82076609e-11]]\n",
            "\n",
            "Iteration 39:\n",
            "\n",
            "Ant 1 at Position: [8. 8.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 2 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 3 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 4 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 5 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 6 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 7 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 8 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 9 at Position: [7. 7.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 10 at Position: [8. 9.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.45519152e-11 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 1.45519152e-11 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 1.45519152e-11 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 1.45519152e-11\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  1.45519152e-11 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.45519152e-11 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.45519152e-11 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 2.91038305e-11\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.91038305e-11 2.50000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.91038305e-11]]\n",
            "\n",
            "Iteration 40:\n",
            "\n",
            "Ant 1 at Position: [6. 6.]\n",
            "Selected Next Position: (9, 0)\n",
            "\n",
            "Ant 2 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 3 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 4 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 5 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 6 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 7 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 8 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 9 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 10 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[7.27595761e-12 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 7.27595761e-12 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 7.27595761e-12 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 7.27595761e-12\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  7.27595761e-12 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 7.27595761e-12 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.45519152e-11 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 1.45519152e-11\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.45519152e-11 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.45519152e-11]]\n",
            "\n",
            "Iteration 41:\n",
            "\n",
            "Ant 1 at Position: [9. 0.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 2 at Position: [7. 7.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 3 at Position: [8. 8.]\n",
            "Selected Next Position: (2, 1)\n",
            "\n",
            "Ant 4 at Position: [9. 9.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 5 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 6 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 7 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 8 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 9 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 10 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[3.63797881e-12 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 3.63797881e-12 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 3.63797881e-12 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 3.63797881e-12\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  3.63797881e-12 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 7.27595761e-12 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 7.27595761e-12 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 7.27595761e-12\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  7.27595761e-12 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 7.27595761e-12]]\n",
            "\n",
            "Iteration 42:\n",
            "\n",
            "Ant 1 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 2 at Position: [8. 8.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 3 at Position: [2. 1.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 4 at Position: [7. 7.]\n",
            "Selected Next Position: (3, 2)\n",
            "\n",
            "Ant 5 at Position: [9. 9.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 6 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 7 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 8 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 9 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 10 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.81898940e-12 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 1.81898940e-12 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 1.81898940e-12 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 1.81898940e-12\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  3.63797881e-12 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 3.63797881e-12 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 3.63797881e-12 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 3.63797881e-12\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  3.63797881e-12 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 3.63797881e-12]]\n",
            "\n",
            "Iteration 43:\n",
            "\n",
            "Ant 1 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 2 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 3 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 4 at Position: [3. 2.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 5 at Position: [8. 8.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 6 at Position: [9. 9.]\n",
            "Selected Next Position: (5, 4)\n",
            "\n",
            "Ant 7 at Position: [0. 0.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 8 at Position: [1. 1.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 9 at Position: [2. 2.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 10 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[9.09494702e-13 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 9.09494702e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 9.09494702e-13 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 1.81898940e-12\n",
            "  2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  1.81898940e-12 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  2.50000000e-01 1.81898940e-12 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.81898940e-12 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 1.81898940e-12\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.81898940e-12 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 1.81898940e-12]]\n",
            "\n",
            "Iteration 44:\n",
            "\n",
            "Ant 1 at Position: [4. 4.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 2 at Position: [5. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 3 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 4 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 5 at Position: [9. 9.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 6 at Position: [5. 4.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 7 at Position: [8. 8.]\n",
            "Selected Next Position: (6, 5)\n",
            "\n",
            "Ant 8 at Position: [0. 0.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 9 at Position: [1. 1.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 10 at Position: [2. 2.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[9.09494702e-13 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 9.09494702e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 9.09494702e-13 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 9.09494702e-13\n",
            "  2.50000000e-01 1.00000000e+00 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  9.09494702e-13 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  2.50000000e-01 9.09494702e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01 9.09494702e-13 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 9.09494702e-13\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  9.09494702e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 9.09494702e-13]]\n",
            "\n",
            "Iteration 45:\n",
            "\n",
            "Ant 1 at Position: [3. 3.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 2 at Position: [4. 4.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 3 at Position: [5. 5.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 4 at Position: [6. 6.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 5 at Position: [7. 7.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 6 at Position: [8. 8.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 7 at Position: [6. 5.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Ant 8 at Position: [9. 9.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 9 at Position: [2. 6.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 10 at Position: [3. 6.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[4.54747351e-13 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 4.54747351e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 4.54747351e-13 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 4.54747351e-13\n",
            "  2.50000000e-01 1.00000000e+00 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  4.54747351e-13 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  2.50000000e-01 4.54747351e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01 4.54747351e-13 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 4.54747351e-13\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  9.09494702e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 9.09494702e-13]]\n",
            "\n",
            "Iteration 46:\n",
            "\n",
            "Ant 1 at Position: [6. 2.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 2 at Position: [6. 3.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 3 at Position: [0. 0.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Ant 4 at Position: [1. 1.]\n",
            "Selected Next Position: (2, 3)\n",
            "\n",
            "Ant 5 at Position: [2. 2.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 6 at Position: [3. 3.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 7 at Position: [4. 4.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Ant 8 at Position: [5. 5.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 9 at Position: [6. 6.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Ant 10 at Position: [7. 7.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[4.54747351e-13 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 4.54747351e-13 1.25000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 4.54747351e-13\n",
            "  2.50000000e-01 5.00000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  4.54747351e-13 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.50000000e-01 4.54747351e-13 2.50000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  5.00000000e-01 2.50000000e-01 4.54747351e-13 2.50000000e-01\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 4.54747351e-13\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  4.54747351e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 4.54747351e-13]]\n",
            "\n",
            "Iteration 47:\n",
            "\n",
            "Ant 1 at Position: [8. 8.]\n",
            "Selected Next Position: (7, 5)\n",
            "\n",
            "Ant 2 at Position: [9. 9.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 3 at Position: [1. 1.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 4 at Position: [2. 3.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Ant 5 at Position: [3. 5.]\n",
            "Selected Next Position: (0, 3)\n",
            "\n",
            "Ant 6 at Position: [5. 3.]\n",
            "Selected Next Position: (3, 0)\n",
            "\n",
            "Ant 7 at Position: [4. 6.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 8 at Position: [6. 4.]\n",
            "Selected Next Position: (2, 2)\n",
            "\n",
            "Ant 9 at Position: [5. 7.]\n",
            "Selected Next Position: (3, 3)\n",
            "\n",
            "Ant 10 at Position: [6. 8.]\n",
            "Selected Next Position: (4, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 5.00000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.50000000e-01 4.54747351e-13 2.50000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  5.00000000e-01 2.50000000e-01 4.54747351e-13 2.50000000e-01\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 2.50000000e-01 4.54747351e-13\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  4.54747351e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 4.54747351e-13]]\n",
            "\n",
            "Iteration 48:\n",
            "\n",
            "Ant 1 at Position: [7. 5.]\n",
            "Selected Next Position: (5, 5)\n",
            "\n",
            "Ant 2 at Position: [0. 7.]\n",
            "Selected Next Position: (6, 6)\n",
            "\n",
            "Ant 3 at Position: [7. 0.]\n",
            "Selected Next Position: (7, 7)\n",
            "\n",
            "Ant 4 at Position: [8. 6.]\n",
            "Selected Next Position: (8, 8)\n",
            "\n",
            "Ant 5 at Position: [0. 3.]\n",
            "Selected Next Position: (9, 9)\n",
            "\n",
            "Ant 6 at Position: [3. 0.]\n",
            "Selected Next Position: (8, 7)\n",
            "\n",
            "Ant 7 at Position: [0. 0.]\n",
            "Selected Next Position: (2, 5)\n",
            "\n",
            "Ant 8 at Position: [2. 2.]\n",
            "Selected Next Position: (5, 2)\n",
            "\n",
            "Ant 9 at Position: [3. 3.]\n",
            "Selected Next Position: (2, 5)\n",
            "\n",
            "Ant 10 at Position: [4. 4.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.00000000e+00 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 5.00000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  5.00000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 49:\n",
            "\n",
            "Ant 1 at Position: [5. 5.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Ant 2 at Position: [6. 6.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Ant 3 at Position: [7. 7.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 4 at Position: [8. 8.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 5 at Position: [9. 9.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 6 at Position: [8. 7.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 7 at Position: [2. 5.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 8 at Position: [5. 2.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 9 at Position: [2. 5.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 10 at Position: [6. 8.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [1.00000000e+00 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  2.50000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 5.00000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 5.00000000e-01\n",
            "  5.00000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 50:\n",
            "\n",
            "Ant 1 at Position: [8. 6.]\n",
            "Selected Next Position: (7, 5)\n",
            "\n",
            "Ant 2 at Position: [6. 8.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 3 at Position: [1. 9.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 4 at Position: [9. 1.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 5 at Position: [1. 9.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 6 at Position: [2. 4.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 7 at Position: [4. 2.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 8 at Position: [2. 4.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Ant 9 at Position: [4. 2.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 10 at Position: [5. 7.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 2.50000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [1.00000000e+00 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 5.00000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 5.00000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  5.00000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 51:\n",
            "\n",
            "Ant 1 at Position: [7. 5.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 2 at Position: [2. 4.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 3 at Position: [1. 5.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 4 at Position: [1. 7.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 5 at Position: [5. 1.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 6 at Position: [7. 1.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 7 at Position: [1. 7.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 8 at Position: [3. 6.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 9 at Position: [6. 3.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Ant 10 at Position: [3. 6.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 2.50000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 2.50000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  5.00000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 52:\n",
            "\n",
            "Ant 1 at Position: [7. 9.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 2 at Position: [9. 7.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 3 at Position: [7. 9.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 4 at Position: [0. 2.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 5 at Position: [2. 0.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Ant 6 at Position: [0. 2.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 7 at Position: [2. 0.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Ant 8 at Position: [3. 5.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 9 at Position: [4. 6.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Ant 10 at Position: [5. 3.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 2.50000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [5.00000000e-01 1.00000000e+00 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 1.25000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 1.00000000e+00 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 53:\n",
            "\n",
            "Ant 1 at Position: [6. 4.]\n",
            "Selected Next Position: (7, 5)\n",
            "\n",
            "Ant 2 at Position: [3. 5.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Ant 3 at Position: [7. 1.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Ant 4 at Position: [1. 7.]\n",
            "Selected Next Position: (2, 9)\n",
            "\n",
            "Ant 5 at Position: [4. 6.]\n",
            "Selected Next Position: (9, 2)\n",
            "\n",
            "Ant 6 at Position: [4. 8.]\n",
            "Selected Next Position: (2, 9)\n",
            "\n",
            "Ant 7 at Position: [8. 4.]\n",
            "Selected Next Position: (9, 2)\n",
            "\n",
            "Ant 8 at Position: [4. 8.]\n",
            "Selected Next Position: (2, 9)\n",
            "\n",
            "Ant 9 at Position: [8. 4.]\n",
            "Selected Next Position: (3, 7)\n",
            "\n",
            "Ant 10 at Position: [5. 7.]\n",
            "Selected Next Position: (7, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 2.50000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [5.00000000e-01 1.00000000e+00 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 1.25000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00 1.25000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 54:\n",
            "\n",
            "Ant 1 at Position: [7. 5.]\n",
            "Selected Next Position: (3, 7)\n",
            "\n",
            "Ant 2 at Position: [8. 6.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 3 at Position: [5. 7.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 4 at Position: [2. 9.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 5 at Position: [9. 2.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 6 at Position: [2. 9.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 7 at Position: [9. 2.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 8 at Position: [2. 9.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 9 at Position: [3. 7.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 10 at Position: [7. 3.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 2.50000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 2.50000000e-01\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 2.50000000e-01]\n",
            " [2.50000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  5.00000000e-01 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  1.00000000e+00 1.25000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00 1.25000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 55:\n",
            "\n",
            "Ant 1 at Position: [3. 7.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 2 at Position: [0. 8.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 3 at Position: [8. 0.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 4 at Position: [0. 8.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 5 at Position: [1. 3.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 6 at Position: [3. 1.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 7 at Position: [1. 3.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 8 at Position: [3. 1.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 9 at Position: [8. 0.]\n",
            "Selected Next Position: (7, 3)\n",
            "\n",
            "Ant 10 at Position: [2. 8.]\n",
            "Selected Next Position: (3, 7)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 2.50000000e-01\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 1.25000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 2.50000000e-01\n",
            "  1.00000000e+00 1.25000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00 1.25000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.25000000e-01 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 56:\n",
            "\n",
            "Ant 1 at Position: [8. 2.]\n",
            "Selected Next Position: (7, 3)\n",
            "\n",
            "Ant 2 at Position: [2. 8.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 3 at Position: [8. 2.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 4 at Position: [9. 1.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 5 at Position: [0. 2.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 6 at Position: [1. 9.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 7 at Position: [2. 0.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 8 at Position: [9. 1.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 9 at Position: [7. 3.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 10 at Position: [3. 7.]\n",
            "Selected Next Position: (5, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  6.25000000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.25000000e-01]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 1.25000000e-01\n",
            "  1.00000000e+00 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  5.00000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [1.25000000e-01 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.00000000e+00 1.25000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.25000000e-01 2.50000000e-01 1.00000000e+00\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 57:\n",
            "\n",
            "Ant 1 at Position: [7. 3.]\n",
            "Selected Next Position: (8, 5)\n",
            "\n",
            "Ant 2 at Position: [0. 8.]\n",
            "Selected Next Position: (5, 8)\n",
            "\n",
            "Ant 3 at Position: [8. 0.]\n",
            "Selected Next Position: (8, 5)\n",
            "\n",
            "Ant 4 at Position: [0. 8.]\n",
            "Selected Next Position: (5, 8)\n",
            "\n",
            "Ant 5 at Position: [1. 3.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 6 at Position: [3. 1.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 7 at Position: [1. 3.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 8 at Position: [6. 4.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 9 at Position: [3. 1.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 10 at Position: [5. 8.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  6.25000000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 1.25000000e-01]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.25000000e-01 1.00000000e+00\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [1.25000000e-01 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 2.50000000e-01 1.25000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.25000000e-01 2.50000000e-01 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 58:\n",
            "\n",
            "Ant 1 at Position: [8. 5.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Ant 2 at Position: [5. 8.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Ant 3 at Position: [8. 5.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 4 at Position: [5. 8.]\n",
            "Selected Next Position: (9, 2)\n",
            "\n",
            "Ant 5 at Position: [3. 9.]\n",
            "Selected Next Position: (2, 9)\n",
            "\n",
            "Ant 6 at Position: [9. 3.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Ant 7 at Position: [3. 9.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Ant 8 at Position: [9. 3.]\n",
            "Selected Next Position: (9, 2)\n",
            "\n",
            "Ant 9 at Position: [3. 9.]\n",
            "Selected Next Position: (3, 7)\n",
            "\n",
            "Ant 10 at Position: [4. 8.]\n",
            "Selected Next Position: (7, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  6.25000000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 6.25000000e-02\n",
            "  1.00000000e+00 1.25000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [1.25000000e-01 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  1.25000000e-01 2.50000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 59:\n",
            "\n",
            "Ant 1 at Position: [8. 4.]\n",
            "Selected Next Position: (3, 7)\n",
            "\n",
            "Ant 2 at Position: [4. 6.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 3 at Position: [4. 8.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 4 at Position: [9. 2.]\n",
            "Selected Next Position: (7, 3)\n",
            "\n",
            "Ant 5 at Position: [2. 9.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 6 at Position: [6. 8.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 7 at Position: [8. 6.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 8 at Position: [9. 2.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 9 at Position: [3. 7.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 10 at Position: [7. 3.]\n",
            "Selected Next Position: (8, 5)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 1.25000000e-01]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [1.00000000e+00 1.00000000e+00 2.50000000e-01 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.25000000e-01 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 1.25000000e-01 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [6.25000000e-02 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  1.25000000e-01 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 60:\n",
            "\n",
            "Ant 1 at Position: [3. 7.]\n",
            "Selected Next Position: (5, 8)\n",
            "\n",
            "Ant 2 at Position: [8. 0.]\n",
            "Selected Next Position: (8, 5)\n",
            "\n",
            "Ant 3 at Position: [0. 8.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 4 at Position: [7. 3.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 5 at Position: [9. 3.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 6 at Position: [3. 9.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 7 at Position: [9. 3.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 8 at Position: [6. 4.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 9 at Position: [3. 9.]\n",
            "Selected Next Position: (7, 5)\n",
            "\n",
            "Ant 10 at Position: [8. 5.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [1.00000000e+00 1.00000000e+00 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [6.25000000e-02 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  1.25000000e-01 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 61:\n",
            "\n",
            "Ant 1 at Position: [5. 8.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 2 at Position: [8. 5.]\n",
            "Selected Next Position: (9, 6)\n",
            "\n",
            "Ant 3 at Position: [1. 9.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 4 at Position: [9. 1.]\n",
            "Selected Next Position: (9, 6)\n",
            "\n",
            "Ant 5 at Position: [1. 9.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 6 at Position: [4. 2.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Ant 7 at Position: [2. 4.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 8 at Position: [4. 2.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Ant 9 at Position: [7. 5.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 10 at Position: [2. 4.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.25000000e-01 1.00000000e+00 1.00000000e+00 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 1.00000000e+00 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [1.00000000e+00 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [6.25000000e-02 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  1.25000000e-01 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 1.00000000e+00 2.50000000e-01 5.00000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 62:\n",
            "\n",
            "Ant 1 at Position: [6. 9.]\n",
            "Selected Next Position: (5, 9)\n",
            "\n",
            "Ant 2 at Position: [9. 6.]\n",
            "Selected Next Position: (9, 5)\n",
            "\n",
            "Ant 3 at Position: [6. 9.]\n",
            "Selected Next Position: (5, 9)\n",
            "\n",
            "Ant 4 at Position: [9. 6.]\n",
            "Selected Next Position: (9, 5)\n",
            "\n",
            "Ant 5 at Position: [6. 9.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 6 at Position: [0. 4.]\n",
            "Selected Next Position: (5, 9)\n",
            "\n",
            "Ant 7 at Position: [4. 0.]\n",
            "Selected Next Position: (0, 5)\n",
            "\n",
            "Ant 8 at Position: [0. 4.]\n",
            "Selected Next Position: (5, 0)\n",
            "\n",
            "Ant 9 at Position: [4. 0.]\n",
            "Selected Next Position: (0, 5)\n",
            "\n",
            "Ant 10 at Position: [0. 4.]\n",
            "Selected Next Position: (5, 0)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [1.25000000e-01 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 1.00000000e+00 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 5.00000000e-01 5.00000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 2.50000000e-01]\n",
            " [6.25000000e-02 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  1.25000000e-01 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 2.50000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 63:\n",
            "\n",
            "Ant 1 at Position: [5. 9.]\n",
            "Selected Next Position: (5, 2)\n",
            "\n",
            "Ant 2 at Position: [9. 5.]\n",
            "Selected Next Position: (2, 5)\n",
            "\n",
            "Ant 3 at Position: [5. 9.]\n",
            "Selected Next Position: (5, 2)\n",
            "\n",
            "Ant 4 at Position: [9. 5.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 5 at Position: [9. 7.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 6 at Position: [5. 9.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 7 at Position: [0. 5.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 8 at Position: [5. 0.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 9 at Position: [0. 5.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 10 at Position: [5. 0.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 3.12500000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 1.00000000e+00 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 5.00000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [6.25000000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 1.00000000e+00 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 5.00000000e-01 1.25000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [1.00000000e+00 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 1.25000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 64:\n",
            "\n",
            "Ant 1 at Position: [5. 2.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 2 at Position: [2. 5.]\n",
            "Selected Next Position: (0, 6)\n",
            "\n",
            "Ant 3 at Position: [5. 2.]\n",
            "Selected Next Position: (6, 0)\n",
            "\n",
            "Ant 4 at Position: [7. 9.]\n",
            "Selected Next Position: (0, 6)\n",
            "\n",
            "Ant 5 at Position: [0. 2.]\n",
            "Selected Next Position: (6, 0)\n",
            "\n",
            "Ant 6 at Position: [9. 7.]\n",
            "Selected Next Position: (0, 6)\n",
            "\n",
            "Ant 7 at Position: [2. 0.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 8 at Position: [7. 9.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 9 at Position: [0. 2.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 10 at Position: [8. 4.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 3.12500000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 1.25000000e-01 1.00000000e+00 1.25000000e-01\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 5.00000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 1.00000000e+00 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 1.25000000e-01 1.25000000e-01 2.50000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 1.00000000e+00 5.00000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 1.00000000e+00 2.50000000e-01 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 1.25000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 65:\n",
            "\n",
            "Ant 1 at Position: [2. 0.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 2 at Position: [0. 6.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 3 at Position: [6. 0.]\n",
            "Selected Next Position: (1, 6)\n",
            "\n",
            "Ant 4 at Position: [0. 6.]\n",
            "Selected Next Position: (1, 8)\n",
            "\n",
            "Ant 5 at Position: [6. 0.]\n",
            "Selected Next Position: (6, 1)\n",
            "\n",
            "Ant 6 at Position: [0. 6.]\n",
            "Selected Next Position: (8, 1)\n",
            "\n",
            "Ant 7 at Position: [1. 5.]\n",
            "Selected Next Position: (1, 6)\n",
            "\n",
            "Ant 8 at Position: [5. 1.]\n",
            "Selected Next Position: (1, 8)\n",
            "\n",
            "Ant 9 at Position: [1. 5.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 10 at Position: [5. 1.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 3.12500000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 1.25000000e-01\n",
            "  2.50000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 2.50000000e-01 1.00000000e+00\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.25000000e-01 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 1.00000000e+00 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 1.25000000e-01 1.25000000e-01 1.25000000e-01\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 2.50000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 2.50000000e-01 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 1.25000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 66:\n",
            "\n",
            "Ant 1 at Position: [5. 3.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 2 at Position: [1. 5.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 3 at Position: [1. 6.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 4 at Position: [1. 8.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 5 at Position: [6. 1.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 6 at Position: [8. 1.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 7 at Position: [1. 6.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 8 at Position: [1. 8.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 9 at Position: [2. 6.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 10 at Position: [6. 2.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 3.12500000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 1.25000000e-01\n",
            "  2.50000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 1.00000000e+00 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 1.25000000e-01 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 1.25000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 2.50000000e-01 2.50000000e-01 1.25000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 67:\n",
            "\n",
            "Ant 1 at Position: [2. 6.]\n",
            "Selected Next Position: (9, 6)\n",
            "\n",
            "Ant 2 at Position: [6. 2.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 3 at Position: [2. 6.]\n",
            "Selected Next Position: (9, 6)\n",
            "\n",
            "Ant 4 at Position: [2. 8.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 5 at Position: [8. 2.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 6 at Position: [2. 8.]\n",
            "Selected Next Position: (1, 4)\n",
            "\n",
            "Ant 7 at Position: [3. 5.]\n",
            "Selected Next Position: (4, 1)\n",
            "\n",
            "Ant 8 at Position: [5. 3.]\n",
            "Selected Next Position: (1, 4)\n",
            "\n",
            "Ant 9 at Position: [8. 2.]\n",
            "Selected Next Position: (4, 1)\n",
            "\n",
            "Ant 10 at Position: [3. 5.]\n",
            "Selected Next Position: (1, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 3.12500000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 1.25000000e-01 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.25000000e-01 6.25000000e-02 2.50000000e-01 1.25000000e-01\n",
            "  2.50000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 2.50000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 1.25000000e-01 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 5.00000000e-01 1.25000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 2.50000000e-01 6.25000000e-02 1.25000000e-01\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 68:\n",
            "\n",
            "Ant 1 at Position: [9. 6.]\n",
            "Selected Next Position: (4, 1)\n",
            "\n",
            "Ant 2 at Position: [6. 9.]\n",
            "Selected Next Position: (6, 0)\n",
            "\n",
            "Ant 3 at Position: [9. 6.]\n",
            "Selected Next Position: (9, 5)\n",
            "\n",
            "Ant 4 at Position: [4. 8.]\n",
            "Selected Next Position: (0, 6)\n",
            "\n",
            "Ant 5 at Position: [6. 9.]\n",
            "Selected Next Position: (5, 9)\n",
            "\n",
            "Ant 6 at Position: [1. 4.]\n",
            "Selected Next Position: (6, 0)\n",
            "\n",
            "Ant 7 at Position: [4. 1.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 8 at Position: [1. 4.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 9 at Position: [4. 1.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 10 at Position: [1. 4.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 3.12500000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 6.25000000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.25000000e-01 3.12500000e-02 2.50000000e-01 1.25000000e-01\n",
            "  2.50000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02]\n",
            " [2.50000000e-01 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 1.25000000e-01 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 69:\n",
            "\n",
            "Ant 1 at Position: [4. 1.]\n",
            "Selected Next Position: (9, 6)\n",
            "\n",
            "Ant 2 at Position: [6. 0.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 3 at Position: [9. 5.]\n",
            "Selected Next Position: (8, 1)\n",
            "\n",
            "Ant 4 at Position: [0. 6.]\n",
            "Selected Next Position: (1, 8)\n",
            "\n",
            "Ant 5 at Position: [5. 9.]\n",
            "Selected Next Position: (8, 1)\n",
            "\n",
            "Ant 6 at Position: [6. 0.]\n",
            "Selected Next Position: (1, 8)\n",
            "\n",
            "Ant 7 at Position: [5. 1.]\n",
            "Selected Next Position: (8, 1)\n",
            "\n",
            "Ant 8 at Position: [9. 7.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 9 at Position: [1. 5.]\n",
            "Selected Next Position: (1, 8)\n",
            "\n",
            "Ant 10 at Position: [5. 1.]\n",
            "Selected Next Position: (3, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 3.12500000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 6.25000000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  1.25000000e-01 1.56250000e-02 2.50000000e-01 1.25000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  5.00000000e-01 3.12500000e-02]\n",
            " [2.50000000e-01 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.00000000e+00 1.25000000e-01 3.12500000e-02 6.25000000e-02\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 70:\n",
            "\n",
            "Ant 1 at Position: [9. 6.]\n",
            "Selected Next Position: (8, 3)\n",
            "\n",
            "Ant 2 at Position: [1. 5.]\n",
            "Selected Next Position: (3, 8)\n",
            "\n",
            "Ant 3 at Position: [8. 1.]\n",
            "Selected Next Position: (8, 3)\n",
            "\n",
            "Ant 4 at Position: [1. 8.]\n",
            "Selected Next Position: (3, 8)\n",
            "\n",
            "Ant 5 at Position: [8. 1.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 6 at Position: [1. 8.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 7 at Position: [8. 1.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 8 at Position: [6. 2.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 9 at Position: [1. 8.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 10 at Position: [3. 8.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  1.25000000e-01 2.50000000e-01 6.25000000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.56250000e-02\n",
            "  1.25000000e-01 1.56250000e-02 2.50000000e-01 1.25000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.25000000e-01 3.12500000e-02]\n",
            " [2.50000000e-01 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.00000000e+00]\n",
            " [2.50000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  1.00000000e+00 1.25000000e-01 3.12500000e-02 6.25000000e-02\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 71:\n",
            "\n",
            "Ant 1 at Position: [8. 3.]\n",
            "Selected Next Position: (4, 9)\n",
            "\n",
            "Ant 2 at Position: [3. 8.]\n",
            "Selected Next Position: (9, 4)\n",
            "\n",
            "Ant 3 at Position: [8. 3.]\n",
            "Selected Next Position: (4, 9)\n",
            "\n",
            "Ant 4 at Position: [3. 8.]\n",
            "Selected Next Position: (9, 4)\n",
            "\n",
            "Ant 5 at Position: [1. 3.]\n",
            "Selected Next Position: (4, 9)\n",
            "\n",
            "Ant 6 at Position: [3. 1.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 7 at Position: [9. 1.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Ant 8 at Position: [9. 3.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 9 at Position: [0. 2.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Ant 10 at Position: [1. 3.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[2.27373675e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 6.25000000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 2.27373675e-13 2.50000000e-01 1.56250000e-02\n",
            "  1.25000000e-01 1.56250000e-02 2.50000000e-01 1.25000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.25000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 6.25000000e-02\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 6.25000000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 6.25000000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 1.25000000e-01 3.12500000e-02 6.25000000e-02\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 72:\n",
            "\n",
            "Ant 1 at Position: [4. 9.]\n",
            "Selected Next Position: (9, 5)\n",
            "\n",
            "Ant 2 at Position: [9. 4.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 3 at Position: [4. 9.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Ant 4 at Position: [9. 4.]\n",
            "Selected Next Position: (5, 9)\n",
            "\n",
            "Ant 5 at Position: [4. 9.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Ant 6 at Position: [4. 0.]\n",
            "Selected Next Position: (7, 5)\n",
            "\n",
            "Ant 7 at Position: [0. 4.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 8 at Position: [4. 0.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 9 at Position: [0. 4.]\n",
            "Selected Next Position: (0, 0)\n",
            "\n",
            "Ant 10 at Position: [4. 0.]\n",
            "Selected Next Position: (1, 1)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 6.25000000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.56250000e-02\n",
            "  1.25000000e-01 1.56250000e-02 2.50000000e-01 1.25000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.25000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 1.25000000e-01 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 3.12500000e-02\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [5.00000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  5.00000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 73:\n",
            "\n",
            "Ant 1 at Position: [9. 5.]\n",
            "Selected Next Position: (9, 8)\n",
            "\n",
            "Ant 2 at Position: [4. 2.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 3 at Position: [5. 7.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 4 at Position: [5. 9.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 5 at Position: [6. 8.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 6 at Position: [7. 5.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 7 at Position: [7. 9.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 8 at Position: [9. 7.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Ant 9 at Position: [0. 0.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 10 at Position: [1. 1.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 6.25000000e-02 1.25000000e-01\n",
            "  3.12500000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.25000000e-01 1.56250000e-02 2.50000000e-01 1.25000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.25000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 1.25000000e-01 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 3.12500000e-02\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [2.50000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 74:\n",
            "\n",
            "Ant 1 at Position: [9. 8.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 2 at Position: [0. 7.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 3 at Position: [7. 0.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 4 at Position: [0. 7.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 5 at Position: [3. 1.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 6 at Position: [1. 3.]\n",
            "Selected Next Position: (1, 4)\n",
            "\n",
            "Ant 7 at Position: [3. 1.]\n",
            "Selected Next Position: (4, 1)\n",
            "\n",
            "Ant 8 at Position: [4. 6.]\n",
            "Selected Next Position: (1, 4)\n",
            "\n",
            "Ant 9 at Position: [6. 4.]\n",
            "Selected Next Position: (4, 1)\n",
            "\n",
            "Ant 10 at Position: [4. 6.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 2.50000000e-01 6.25000000e-02 1.25000000e-01\n",
            "  1.56250000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  3.12500000e-02 1.56250000e-02 2.50000000e-01 1.25000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  1.25000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 3.12500000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 3.12500000e-02\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  1.56250000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [2.50000000e-01 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [1.56250000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 75:\n",
            "\n",
            "Ant 1 at Position: [6. 4.]\n",
            "Selected Next Position: (1, 4)\n",
            "\n",
            "Ant 2 at Position: [8. 0.]\n",
            "Selected Next Position: (0, 5)\n",
            "\n",
            "Ant 3 at Position: [0. 8.]\n",
            "Selected Next Position: (5, 0)\n",
            "\n",
            "Ant 4 at Position: [7. 9.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 5 at Position: [8. 0.]\n",
            "Selected Next Position: (0, 5)\n",
            "\n",
            "Ant 6 at Position: [1. 4.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 7 at Position: [4. 1.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 8 at Position: [1. 4.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 9 at Position: [4. 1.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Ant 10 at Position: [8. 6.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 1.56250000e-02 2.50000000e-01 1.25000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 1.00000000e+00\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  1.25000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 3.12500000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 1.00000000e+00\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 3.12500000e-02 1.25000000e-01 6.25000000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 3.12500000e-02\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 2.50000000e-01 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [1.56250000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 76:\n",
            "\n",
            "Ant 1 at Position: [1. 4.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Ant 2 at Position: [0. 5.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 3 at Position: [5. 0.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 4 at Position: [7. 0.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 5 at Position: [0. 5.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 6 at Position: [0. 7.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 7 at Position: [7. 0.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 8 at Position: [6. 3.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 9 at Position: [3. 6.]\n",
            "Selected Next Position: (2, 7)\n",
            "\n",
            "Ant 10 at Position: [6. 3.]\n",
            "Selected Next Position: (4, 7)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 1.56250000e-02 2.50000000e-01 3.12500000e-02\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 5.00000000e-01\n",
            "  6.25000000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 3.12500000e-02 3.12500000e-02\n",
            "  1.25000000e-01 3.12500000e-02]\n",
            " [3.12500000e-02 3.12500000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 3.12500000e-02\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 3.12500000e-02\n",
            "  1.56250000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 3.12500000e-02 1.00000000e+00 3.12500000e-02\n",
            "  1.00000000e+00 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [1.56250000e-02 6.25000000e-02 6.25000000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 77:\n",
            "\n",
            "Ant 1 at Position: [3. 6.]\n",
            "Selected Next Position: (7, 2)\n",
            "\n",
            "Ant 2 at Position: [6. 3.]\n",
            "Selected Next Position: (7, 4)\n",
            "\n",
            "Ant 3 at Position: [7. 1.]\n",
            "Selected Next Position: (2, 7)\n",
            "\n",
            "Ant 4 at Position: [1. 7.]\n",
            "Selected Next Position: (4, 7)\n",
            "\n",
            "Ant 5 at Position: [7. 1.]\n",
            "Selected Next Position: (7, 4)\n",
            "\n",
            "Ant 6 at Position: [1. 7.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 7 at Position: [5. 3.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 8 at Position: [7. 1.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 9 at Position: [2. 7.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 10 at Position: [4. 7.]\n",
            "Selected Next Position: (4, 1)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.56250000e-02 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 1.56250000e-02 2.50000000e-01 3.12500000e-02\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.12500000e-02 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.12500000e-02 3.12500000e-02 3.12500000e-02\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 3.12500000e-02 1.25000000e-01 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 3.12500000e-02\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 3.12500000e-02\n",
            "  1.56250000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 3.12500000e-02 5.00000000e-01 3.12500000e-02\n",
            "  2.50000000e-01 3.12500000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [1.56250000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 78:\n",
            "\n",
            "Ant 1 at Position: [7. 2.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Ant 2 at Position: [7. 4.]\n",
            "Selected Next Position: (7, 5)\n",
            "\n",
            "Ant 3 at Position: [2. 7.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Ant 4 at Position: [4. 7.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 5 at Position: [7. 4.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 6 at Position: [2. 8.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 7 at Position: [8. 2.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 8 at Position: [2. 8.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 9 at Position: [3. 9.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 10 at Position: [4. 1.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 7.81250000e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 1.56250000e-02 2.50000000e-01 3.12500000e-02\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [7.81250000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 7.81250000e-03 3.12500000e-02 3.12500000e-02\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 3.12500000e-02 1.25000000e-01 1.56250000e-02\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 3.12500000e-02\n",
            "  1.56250000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.56250000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 3.12500000e-02 5.00000000e-01 3.12500000e-02\n",
            "  2.50000000e-01 1.56250000e-02 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [1.56250000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 79:\n",
            "\n",
            "Ant 1 at Position: [5. 7.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Ant 2 at Position: [7. 5.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 3 at Position: [5. 7.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 4 at Position: [2. 0.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 5 at Position: [0. 2.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 6 at Position: [2. 0.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 7 at Position: [3. 5.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 8 at Position: [5. 3.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 9 at Position: [3. 5.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 10 at Position: [6. 8.]\n",
            "Selected Next Position: (7, 5)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 7.81250000e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 1.56250000e-02 2.50000000e-01 7.81250000e-03\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [7.81250000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  7.81250000e-03 1.25000000e-01 6.25000000e-02 2.50000000e-01\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 7.81250000e-03 3.12500000e-02 3.12500000e-02\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 3.12500000e-02 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 3.12500000e-02\n",
            "  1.56250000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  1.56250000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 1.56250000e-02 5.00000000e-01 3.12500000e-02\n",
            "  2.50000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [1.56250000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 1.56250000e-02 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 80:\n",
            "\n",
            "Ant 1 at Position: [8. 6.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Ant 2 at Position: [5. 3.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Ant 3 at Position: [1. 7.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 4 at Position: [7. 1.]\n",
            "Selected Next Position: (7, 2)\n",
            "\n",
            "Ant 5 at Position: [1. 7.]\n",
            "Selected Next Position: (2, 7)\n",
            "\n",
            "Ant 6 at Position: [2. 4.]\n",
            "Selected Next Position: (7, 2)\n",
            "\n",
            "Ant 7 at Position: [4. 2.]\n",
            "Selected Next Position: (2, 7)\n",
            "\n",
            "Ant 8 at Position: [2. 4.]\n",
            "Selected Next Position: (7, 2)\n",
            "\n",
            "Ant 9 at Position: [4. 2.]\n",
            "Selected Next Position: (3, 7)\n",
            "\n",
            "Ant 10 at Position: [7. 5.]\n",
            "Selected Next Position: (7, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 7.81250000e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 1.56250000e-02 2.50000000e-01 7.81250000e-03\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [7.81250000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 7.81250000e-03 3.12500000e-02 1.56250000e-02\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.56250000e-02 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 3.12500000e-02 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 3.12500000e-02\n",
            "  1.56250000e-02 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  7.81250000e-03 3.12500000e-02]\n",
            " [6.25000000e-02 1.56250000e-02 6.25000000e-02 1.56250000e-02\n",
            "  2.50000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [1.56250000e-02 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 81:\n",
            "\n",
            "Ant 1 at Position: [6. 8.]\n",
            "Selected Next Position: (3, 7)\n",
            "\n",
            "Ant 2 at Position: [8. 6.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 3 at Position: [2. 4.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Ant 4 at Position: [7. 2.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 5 at Position: [2. 7.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 6 at Position: [7. 2.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 7 at Position: [2. 7.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 8 at Position: [7. 2.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 9 at Position: [3. 7.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 10 at Position: [7. 3.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 7.81250000e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  3.90625000e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 3.90625000e-03 2.50000000e-01 7.81250000e-03\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [7.81250000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 7.81250000e-03 3.12500000e-02 7.81250000e-03\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 5.00000000e-01 6.25000000e-02 3.12500000e-02\n",
            "  7.81250000e-03 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  7.81250000e-03 3.12500000e-02]\n",
            " [6.25000000e-02 1.56250000e-02 6.25000000e-02 1.56250000e-02\n",
            "  2.50000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 1.56250000e-02]\n",
            " [7.81250000e-03 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 3.12500000e-02\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 82:\n",
            "\n",
            "Ant 1 at Position: [3. 7.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 2 at Position: [0. 8.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 3 at Position: [4. 6.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 4 at Position: [6. 4.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 5 at Position: [8. 0.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 6 at Position: [0. 8.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Ant 7 at Position: [5. 1.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 8 at Position: [1. 5.]\n",
            "Selected Next Position: (6, 1)\n",
            "\n",
            "Ant 9 at Position: [5. 1.]\n",
            "Selected Next Position: (1, 6)\n",
            "\n",
            "Ant 10 at Position: [1. 5.]\n",
            "Selected Next Position: (6, 1)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 3.90625000e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  3.90625000e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 3.90625000e-03 1.25000000e-01 7.81250000e-03\n",
            "  3.12500000e-02 3.12500000e-02]\n",
            " [3.90625000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 7.81250000e-03 3.12500000e-02 7.81250000e-03\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 1.25000000e-01 6.25000000e-02 3.12500000e-02\n",
            "  7.81250000e-03 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.12500000e-02]\n",
            " [6.25000000e-02 1.56250000e-02 6.25000000e-02 1.56250000e-02\n",
            "  2.50000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [7.81250000e-03 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.12500000e-02 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 83:\n",
            "\n",
            "Ant 1 at Position: [9. 7.]\n",
            "Selected Next Position: (1, 6)\n",
            "\n",
            "Ant 2 at Position: [7. 9.]\n",
            "Selected Next Position: (6, 1)\n",
            "\n",
            "Ant 3 at Position: [9. 7.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 4 at Position: [0. 2.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 5 at Position: [2. 0.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 6 at Position: [6. 8.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 7 at Position: [7. 9.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 8 at Position: [6. 1.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Ant 9 at Position: [1. 6.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 10 at Position: [6. 1.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 3.90625000e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  3.90625000e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  3.12500000e-02 7.81250000e-03]\n",
            " [3.90625000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 7.81250000e-03 7.81250000e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 1.56250000e-02\n",
            "  7.81250000e-03 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.12500000e-02]\n",
            " [6.25000000e-02 7.81250000e-03 6.25000000e-02 1.56250000e-02\n",
            "  2.50000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [7.81250000e-03 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 7.81250000e-03 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 84:\n",
            "\n",
            "Ant 1 at Position: [1. 6.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 2 at Position: [6. 1.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Ant 3 at Position: [1. 9.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 4 at Position: [9. 1.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 5 at Position: [1. 9.]\n",
            "Selected Next Position: (3, 4)\n",
            "\n",
            "Ant 6 at Position: [7. 1.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 7 at Position: [9. 1.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 8 at Position: [3. 6.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 9 at Position: [6. 3.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 10 at Position: [3. 6.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 3.90625000e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 7.81250000e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 3.90625000e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [3.90625000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 7.81250000e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.56250000e-02]\n",
            " [3.12500000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 2.50000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  7.81250000e-03 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.12500000e-02]\n",
            " [6.25000000e-02 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  2.50000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 3.12500000e-02\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 85:\n",
            "\n",
            "Ant 1 at Position: [6. 3.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 2 at Position: [3. 6.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 3 at Position: [1. 7.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 4 at Position: [1. 9.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 5 at Position: [3. 4.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 6 at Position: [8. 0.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 7 at Position: [7. 1.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 8 at Position: [9. 1.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 9 at Position: [0. 8.]\n",
            "Selected Next Position: (4, 7)\n",
            "\n",
            "Ant 10 at Position: [1. 9.]\n",
            "Selected Next Position: (7, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.95312500e-03 5.00000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.95312500e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 3.90625000e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [3.90625000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 3.90625000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 7.81250000e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 3.90625000e-03]\n",
            " [3.12500000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.25000000e-01\n",
            "  3.12500000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  7.81250000e-03 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.12500000e-02]\n",
            " [6.25000000e-02 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  1.25000000e-01 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  6.25000000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 86:\n",
            "\n",
            "Ant 1 at Position: [9. 3.]\n",
            "Selected Next Position: (4, 7)\n",
            "\n",
            "Ant 2 at Position: [3. 9.]\n",
            "Selected Next Position: (7, 4)\n",
            "\n",
            "Ant 3 at Position: [9. 3.]\n",
            "Selected Next Position: (4, 7)\n",
            "\n",
            "Ant 4 at Position: [3. 9.]\n",
            "Selected Next Position: (7, 4)\n",
            "\n",
            "Ant 5 at Position: [1. 3.]\n",
            "Selected Next Position: (4, 7)\n",
            "\n",
            "Ant 6 at Position: [3. 1.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Ant 7 at Position: [0. 2.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Ant 8 at Position: [1. 3.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 9 at Position: [4. 7.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 10 at Position: [7. 4.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.95312500e-03 5.00000000e-01\n",
            "  7.81250000e-03 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.95312500e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 3.90625000e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [3.90625000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 3.90625000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 7.81250000e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 3.90625000e-03]\n",
            " [1.56250000e-02 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  1.56250000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 2.50000000e-01 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  7.81250000e-03 2.50000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.12500000e-02]\n",
            " [6.25000000e-02 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  2.50000000e-01 6.25000000e-02 3.12500000e-02 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 87:\n",
            "\n",
            "Ant 1 at Position: [4. 7.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 2 at Position: [7. 4.]\n",
            "Selected Next Position: (9, 6)\n",
            "\n",
            "Ant 3 at Position: [4. 7.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 4 at Position: [7. 4.]\n",
            "Selected Next Position: (9, 6)\n",
            "\n",
            "Ant 5 at Position: [4. 7.]\n",
            "Selected Next Position: (6, 9)\n",
            "\n",
            "Ant 6 at Position: [8. 4.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 7 at Position: [0. 4.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 8 at Position: [4. 0.]\n",
            "Selected Next Position: (5, 6)\n",
            "\n",
            "Ant 9 at Position: [4. 8.]\n",
            "Selected Next Position: (6, 5)\n",
            "\n",
            "Ant 10 at Position: [0. 4.]\n",
            "Selected Next Position: (5, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.95312500e-03 5.00000000e-01\n",
            "  7.81250000e-03 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.95312500e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 3.90625000e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [3.90625000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  1.56250000e-02 6.25000000e-02]\n",
            " [5.00000000e-01 3.90625000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 7.81250000e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  1.56250000e-02 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 6.25000000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  7.81250000e-03 1.25000000e-01 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.90625000e-03]\n",
            " [6.25000000e-02 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.12500000e-02 2.50000000e-01\n",
            "  3.12500000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  2.50000000e-01 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 88:\n",
            "\n",
            "Ant 1 at Position: [6. 9.]\n",
            "Selected Next Position: (6, 5)\n",
            "\n",
            "Ant 2 at Position: [9. 6.]\n",
            "Selected Next Position: (5, 6)\n",
            "\n",
            "Ant 3 at Position: [6. 9.]\n",
            "Selected Next Position: (6, 5)\n",
            "\n",
            "Ant 4 at Position: [9. 6.]\n",
            "Selected Next Position: (5, 6)\n",
            "\n",
            "Ant 5 at Position: [6. 9.]\n",
            "Selected Next Position: (6, 5)\n",
            "\n",
            "Ant 6 at Position: [4. 0.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 7 at Position: [4. 0.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Ant 8 at Position: [5. 6.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 9 at Position: [6. 5.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 10 at Position: [5. 6.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 1.95312500e-03 5.00000000e-01\n",
            "  3.90625000e-03 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.95312500e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 3.90625000e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [3.90625000e-03 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  3.90625000e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  7.81250000e-03 6.25000000e-02]\n",
            " [5.00000000e-01 3.90625000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 7.81250000e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 1.56250000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  7.81250000e-03 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.90625000e-03]\n",
            " [6.25000000e-02 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 1.56250000e-02 2.50000000e-01\n",
            "  1.56250000e-02 6.25000000e-02 7.81250000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  2.50000000e-01 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 89:\n",
            "\n",
            "Ant 1 at Position: [6. 5.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 2 at Position: [5. 6.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 3 at Position: [6. 5.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 4 at Position: [5. 6.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 5 at Position: [6. 5.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 6 at Position: [8. 2.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 7 at Position: [8. 4.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 8 at Position: [2. 8.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 9 at Position: [4. 8.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 10 at Position: [0. 4.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  3.90625000e-03 6.25000000e-02 6.25000000e-02 6.25000000e-02\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.95312500e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 3.90625000e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.95312500e-03 1.25000000e-01 6.25000000e-02 6.25000000e-02\n",
            "  7.81250000e-03 6.25000000e-02]\n",
            " [5.00000000e-01 3.90625000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 1.95312500e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 1.56250000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  7.81250000e-03 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.90625000e-03]\n",
            " [6.25000000e-02 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 1.56250000e-02 2.50000000e-01\n",
            "  1.56250000e-02 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  2.50000000e-01 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 90:\n",
            "\n",
            "Ant 1 at Position: [4. 2.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Ant 2 at Position: [2. 4.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 3 at Position: [4. 2.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 4 at Position: [2. 0.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 5 at Position: [0. 2.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 6 at Position: [2. 0.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 7 at Position: [3. 5.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 8 at Position: [5. 3.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 9 at Position: [3. 5.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 10 at Position: [8. 6.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  3.90625000e-03 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.95312500e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 3.90625000e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.95312500e-03 1.25000000e-01 1.56250000e-02 6.25000000e-02\n",
            "  7.81250000e-03 6.25000000e-02]\n",
            " [5.00000000e-01 3.90625000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 1.95312500e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 1.56250000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  7.81250000e-03 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 1.95312500e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 3.12500000e-02 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.90625000e-03]\n",
            " [7.81250000e-03 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 1.56250000e-02 2.50000000e-01\n",
            "  1.56250000e-02 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  2.50000000e-01 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 91:\n",
            "\n",
            "Ant 1 at Position: [5. 3.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 2 at Position: [0. 7.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 3 at Position: [7. 0.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 4 at Position: [0. 7.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 5 at Position: [7. 0.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 6 at Position: [0. 7.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 7 at Position: [7. 0.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 8 at Position: [2. 6.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 9 at Position: [6. 2.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 10 at Position: [2. 6.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  3.90625000e-03 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 1.95312500e-03\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.95312500e-03 1.25000000e-01 7.81250000e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 3.90625000e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 1.95312500e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 1.56250000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  7.81250000e-03 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 1.95312500e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.90625000e-03]\n",
            " [7.81250000e-03 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  7.81250000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 3.90625000e-03 6.25000000e-02 7.81250000e-03\n",
            "  2.50000000e-01 6.25000000e-02 7.81250000e-03 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 92:\n",
            "\n",
            "Ant 1 at Position: [6. 2.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 2 at Position: [2. 6.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Ant 3 at Position: [6. 2.]\n",
            "Selected Next Position: (4, 8)\n",
            "\n",
            "Ant 4 at Position: [8. 2.]\n",
            "Selected Next Position: (8, 4)\n",
            "\n",
            "Ant 5 at Position: [2. 8.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 6 at Position: [8. 2.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 7 at Position: [9. 7.]\n",
            "Selected Next Position: (3, 9)\n",
            "\n",
            "Ant 8 at Position: [1. 7.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 9 at Position: [2. 8.]\n",
            "Selected Next Position: (9, 3)\n",
            "\n",
            "Ant 10 at Position: [8. 4.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  3.90625000e-03 6.25000000e-02 6.25000000e-02 7.81250000e-03\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 9.76562500e-04\n",
            "  1.56250000e-02 3.90625000e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.95312500e-03 1.25000000e-01 7.81250000e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 1.95312500e-03 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 1.95312500e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [3.90625000e-03 1.56250000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  1.95312500e-03 1.25000000e-01]\n",
            " [1.25000000e-01 7.81250000e-03 1.25000000e-01 1.95312500e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  3.90625000e-03 3.90625000e-03]\n",
            " [7.81250000e-03 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  2.50000000e-01 6.25000000e-02 7.81250000e-03 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 93:\n",
            "\n",
            "Ant 1 at Position: [4. 8.]\n",
            "Selected Next Position: (3, 1)\n",
            "\n",
            "Ant 2 at Position: [8. 4.]\n",
            "Selected Next Position: (5, 0)\n",
            "\n",
            "Ant 3 at Position: [4. 8.]\n",
            "Selected Next Position: (0, 5)\n",
            "\n",
            "Ant 4 at Position: [8. 4.]\n",
            "Selected Next Position: (5, 0)\n",
            "\n",
            "Ant 5 at Position: [9. 3.]\n",
            "Selected Next Position: (0, 5)\n",
            "\n",
            "Ant 6 at Position: [3. 1.]\n",
            "Selected Next Position: (5, 0)\n",
            "\n",
            "Ant 7 at Position: [3. 9.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 8 at Position: [9. 1.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 9 at Position: [9. 3.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 10 at Position: [1. 3.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  3.90625000e-03 1.56250000e-02 6.25000000e-02 7.81250000e-03\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 9.76562500e-04\n",
            "  1.56250000e-02 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.95312500e-03 1.25000000e-01 7.81250000e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 1.95312500e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [3.90625000e-03 1.56250000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  1.95312500e-03 1.25000000e-01]\n",
            " [1.56250000e-02 1.95312500e-03 1.25000000e-01 1.95312500e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  1.95312500e-03 3.90625000e-03]\n",
            " [7.81250000e-03 3.90625000e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  2.50000000e-01 6.25000000e-02 7.81250000e-03 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 94:\n",
            "\n",
            "Ant 1 at Position: [3. 1.]\n",
            "Selected Next Position: (8, 6)\n",
            "\n",
            "Ant 2 at Position: [5. 0.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 3 at Position: [0. 5.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 4 at Position: [5. 0.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 5 at Position: [0. 5.]\n",
            "Selected Next Position: (6, 8)\n",
            "\n",
            "Ant 6 at Position: [5. 0.]\n",
            "Selected Next Position: (9, 4)\n",
            "\n",
            "Ant 7 at Position: [5. 1.]\n",
            "Selected Next Position: (4, 9)\n",
            "\n",
            "Ant 8 at Position: [1. 5.]\n",
            "Selected Next Position: (9, 4)\n",
            "\n",
            "Ant 9 at Position: [5. 1.]\n",
            "Selected Next Position: (4, 9)\n",
            "\n",
            "Ant 10 at Position: [6. 8.]\n",
            "Selected Next Position: (9, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  3.90625000e-03 1.56250000e-02 6.25000000e-02 7.81250000e-03\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 9.76562500e-04\n",
            "  1.56250000e-02 9.76562500e-04 6.25000000e-02 9.76562500e-04\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  1.95312500e-03 1.25000000e-01 7.81250000e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 1.95312500e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [3.90625000e-03 1.56250000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  1.95312500e-03 3.12500000e-02]\n",
            " [1.56250000e-02 1.95312500e-03 1.25000000e-01 1.95312500e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 7.81250000e-03\n",
            "  6.25000000e-02 3.12500000e-02]\n",
            " [6.25000000e-02 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  9.76562500e-04 3.90625000e-03]\n",
            " [7.81250000e-03 1.95312500e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 6.25000000e-02 7.81250000e-03 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 95:\n",
            "\n",
            "Ant 1 at Position: [8. 6.]\n",
            "Selected Next Position: (9, 5)\n",
            "\n",
            "Ant 2 at Position: [1. 5.]\n",
            "Selected Next Position: (5, 9)\n",
            "\n",
            "Ant 3 at Position: [7. 1.]\n",
            "Selected Next Position: (9, 5)\n",
            "\n",
            "Ant 4 at Position: [1. 7.]\n",
            "Selected Next Position: (5, 9)\n",
            "\n",
            "Ant 5 at Position: [6. 8.]\n",
            "Selected Next Position: (9, 5)\n",
            "\n",
            "Ant 6 at Position: [9. 4.]\n",
            "Selected Next Position: (5, 7)\n",
            "\n",
            "Ant 7 at Position: [4. 9.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Ant 8 at Position: [9. 4.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 9 at Position: [4. 9.]\n",
            "Selected Next Position: (0, 4)\n",
            "\n",
            "Ant 10 at Position: [9. 4.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  9.76562500e-04 1.56250000e-02 6.25000000e-02 7.81250000e-03\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 9.76562500e-04\n",
            "  1.56250000e-02 9.76562500e-04 6.25000000e-02 9.76562500e-04\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  9.76562500e-04 1.25000000e-01 7.81250000e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 1.95312500e-03 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [1.95312500e-03 1.56250000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  1.95312500e-03 3.12500000e-02]\n",
            " [1.56250000e-02 1.95312500e-03 1.25000000e-01 1.95312500e-03\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 3.90625000e-03\n",
            "  6.25000000e-02 7.81250000e-03]\n",
            " [6.25000000e-02 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  9.76562500e-04 3.90625000e-03]\n",
            " [7.81250000e-03 1.95312500e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 7.81250000e-03 7.81250000e-03 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 96:\n",
            "\n",
            "Ant 1 at Position: [9. 5.]\n",
            "Selected Next Position: (4, 0)\n",
            "\n",
            "Ant 2 at Position: [5. 9.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 3 at Position: [9. 5.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 4 at Position: [5. 9.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 5 at Position: [9. 5.]\n",
            "Selected Next Position: (7, 0)\n",
            "\n",
            "Ant 6 at Position: [5. 7.]\n",
            "Selected Next Position: (0, 7)\n",
            "\n",
            "Ant 7 at Position: [0. 4.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 8 at Position: [4. 0.]\n",
            "Selected Next Position: (4, 2)\n",
            "\n",
            "Ant 9 at Position: [0. 4.]\n",
            "Selected Next Position: (5, 1)\n",
            "\n",
            "Ant 10 at Position: [2. 4.]\n",
            "Selected Next Position: (5, 3)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  9.76562500e-04 1.56250000e-02 6.25000000e-02 9.76562500e-04\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 9.76562500e-04\n",
            "  1.56250000e-02 9.76562500e-04 6.25000000e-02 9.76562500e-04\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  9.76562500e-04 1.25000000e-01 7.81250000e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 9.76562500e-04 3.90625000e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [9.76562500e-04 1.56250000e-02 9.76562500e-04 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 7.81250000e-03 1.56250000e-02\n",
            "  1.95312500e-03 3.12500000e-02]\n",
            " [1.56250000e-02 9.76562500e-04 1.25000000e-01 9.76562500e-04\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 3.90625000e-03\n",
            "  6.25000000e-02 7.81250000e-03]\n",
            " [6.25000000e-02 6.25000000e-02 7.81250000e-03 7.81250000e-03\n",
            "  7.81250000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  9.76562500e-04 3.90625000e-03]\n",
            " [1.95312500e-03 1.95312500e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 7.81250000e-03 7.81250000e-03 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 97:\n",
            "\n",
            "Ant 1 at Position: [4. 0.]\n",
            "Selected Next Position: (1, 3)\n",
            "\n",
            "Ant 2 at Position: [0. 7.]\n",
            "Selected Next Position: (1, 5)\n",
            "\n",
            "Ant 3 at Position: [7. 0.]\n",
            "Selected Next Position: (2, 4)\n",
            "\n",
            "Ant 4 at Position: [0. 7.]\n",
            "Selected Next Position: (3, 5)\n",
            "\n",
            "Ant 5 at Position: [7. 0.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 6 at Position: [0. 7.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Ant 7 at Position: [3. 5.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 8 at Position: [4. 2.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Ant 9 at Position: [5. 1.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 10 at Position: [5. 3.]\n",
            "Selected Next Position: (4, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  9.76562500e-04 1.56250000e-02 6.25000000e-02 9.76562500e-04\n",
            "  1.95312500e-03 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 4.88281250e-04\n",
            "  1.56250000e-02 4.88281250e-04 6.25000000e-02 9.76562500e-04\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  4.88281250e-04 1.25000000e-01 7.81250000e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 4.88281250e-04 1.95312500e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [9.76562500e-04 1.56250000e-02 9.76562500e-04 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.95312500e-03 1.56250000e-02\n",
            "  1.95312500e-03 3.12500000e-02]\n",
            " [1.56250000e-02 9.76562500e-04 1.25000000e-01 9.76562500e-04\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 3.90625000e-03\n",
            "  6.25000000e-02 7.81250000e-03]\n",
            " [6.25000000e-02 6.25000000e-02 7.81250000e-03 1.95312500e-03\n",
            "  3.90625000e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  9.76562500e-04 3.90625000e-03]\n",
            " [1.95312500e-03 1.95312500e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 3.90625000e-03]\n",
            " [3.90625000e-03 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 7.81250000e-03 7.81250000e-03 3.90625000e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 98:\n",
            "\n",
            "Ant 1 at Position: [1. 3.]\n",
            "Selected Next Position: (6, 4)\n",
            "\n",
            "Ant 2 at Position: [1. 5.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 3 at Position: [2. 4.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 4 at Position: [3. 5.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 5 at Position: [6. 3.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 6 at Position: [3. 6.]\n",
            "Selected Next Position: (7, 9)\n",
            "\n",
            "Ant 7 at Position: [6. 3.]\n",
            "Selected Next Position: (8, 0)\n",
            "\n",
            "Ant 8 at Position: [4. 6.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 9 at Position: [6. 4.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 10 at Position: [4. 6.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  9.76562500e-04 1.56250000e-02 6.25000000e-02 9.76562500e-04\n",
            "  9.76562500e-04 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 4.88281250e-04\n",
            "  1.56250000e-02 4.88281250e-04 6.25000000e-02 9.76562500e-04\n",
            "  3.12500000e-02 1.95312500e-03]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  4.88281250e-04 1.25000000e-01 1.95312500e-03 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 4.88281250e-04 1.95312500e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [9.76562500e-04 1.56250000e-02 9.76562500e-04 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.95312500e-03 1.56250000e-02\n",
            "  1.95312500e-03 3.12500000e-02]\n",
            " [1.56250000e-02 9.76562500e-04 1.25000000e-01 9.76562500e-04\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 3.90625000e-03\n",
            "  6.25000000e-02 7.81250000e-03]\n",
            " [6.25000000e-02 6.25000000e-02 3.90625000e-03 1.95312500e-03\n",
            "  1.95312500e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  9.76562500e-04 3.90625000e-03]\n",
            " [1.95312500e-03 1.95312500e-03 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 9.76562500e-04]\n",
            " [9.76562500e-04 6.25000000e-02 3.90625000e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 1.95312500e-03 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 7.81250000e-03 7.81250000e-03 1.95312500e-03\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 99:\n",
            "\n",
            "Ant 1 at Position: [6. 4.]\n",
            "Selected Next Position: (6, 2)\n",
            "\n",
            "Ant 2 at Position: [7. 9.]\n",
            "Selected Next Position: (2, 6)\n",
            "\n",
            "Ant 3 at Position: [8. 0.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 4 at Position: [9. 7.]\n",
            "Selected Next Position: (7, 1)\n",
            "\n",
            "Ant 5 at Position: [0. 8.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 6 at Position: [7. 9.]\n",
            "Selected Next Position: (9, 7)\n",
            "\n",
            "Ant 7 at Position: [8. 0.]\n",
            "Selected Next Position: (0, 8)\n",
            "\n",
            "Ant 8 at Position: [2. 6.]\n",
            "Selected Next Position: (1, 7)\n",
            "\n",
            "Ant 9 at Position: [6. 2.]\n",
            "Selected Next Position: (1, 9)\n",
            "\n",
            "Ant 10 at Position: [2. 6.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 9.76562500e-04 5.00000000e-01\n",
            "  9.76562500e-04 1.56250000e-02 6.25000000e-02 9.76562500e-04\n",
            "  4.88281250e-04 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 4.88281250e-04\n",
            "  1.56250000e-02 4.88281250e-04 6.25000000e-02 4.88281250e-04\n",
            "  3.12500000e-02 4.88281250e-04]\n",
            " [9.76562500e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  4.88281250e-04 1.25000000e-01 9.76562500e-04 6.25000000e-02\n",
            "  1.95312500e-03 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 4.88281250e-04 1.95312500e-03 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [9.76562500e-04 1.56250000e-02 9.76562500e-04 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.95312500e-03 1.56250000e-02\n",
            "  1.95312500e-03 3.12500000e-02]\n",
            " [1.56250000e-02 9.76562500e-04 1.25000000e-01 9.76562500e-04\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 3.90625000e-03\n",
            "  6.25000000e-02 7.81250000e-03]\n",
            " [6.25000000e-02 6.25000000e-02 1.95312500e-03 1.95312500e-03\n",
            "  1.95312500e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  9.76562500e-04 3.90625000e-03]\n",
            " [1.95312500e-03 9.76562500e-04 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 9.76562500e-04]\n",
            " [9.76562500e-04 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 9.76562500e-04 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 7.81250000e-03 7.81250000e-03 9.76562500e-04\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Iteration 100:\n",
            "\n",
            "Ant 1 at Position: [6. 2.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 2 at Position: [2. 6.]\n",
            "Selected Next Position: (8, 2)\n",
            "\n",
            "Ant 3 at Position: [1. 9.]\n",
            "Selected Next Position: (2, 8)\n",
            "\n",
            "Ant 4 at Position: [7. 1.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 5 at Position: [9. 1.]\n",
            "Selected Next Position: (2, 0)\n",
            "\n",
            "Ant 6 at Position: [9. 7.]\n",
            "Selected Next Position: (9, 1)\n",
            "\n",
            "Ant 7 at Position: [0. 8.]\n",
            "Selected Next Position: (0, 2)\n",
            "\n",
            "Ant 8 at Position: [1. 7.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Ant 9 at Position: [1. 9.]\n",
            "Selected Next Position: (6, 3)\n",
            "\n",
            "Ant 10 at Position: [8. 2.]\n",
            "Selected Next Position: (3, 6)\n",
            "\n",
            "Pheromone Matrix:\n",
            "[[1.13686838e-13 2.50000000e-01 2.44140625e-04 5.00000000e-01\n",
            "  9.76562500e-04 1.56250000e-02 6.25000000e-02 9.76562500e-04\n",
            "  4.88281250e-04 2.50000000e-01]\n",
            " [2.50000000e-01 1.13686838e-13 2.50000000e-01 4.88281250e-04\n",
            "  1.56250000e-02 4.88281250e-04 6.25000000e-02 4.88281250e-04\n",
            "  3.12500000e-02 4.88281250e-04]\n",
            " [4.88281250e-04 2.50000000e-01 2.27373675e-13 1.25000000e-01\n",
            "  4.88281250e-04 1.25000000e-01 9.76562500e-04 6.25000000e-02\n",
            "  4.88281250e-04 6.25000000e-02]\n",
            " [5.00000000e-01 9.76562500e-04 2.50000000e-01 2.27373675e-13\n",
            "  1.25000000e-01 4.88281250e-04 4.88281250e-04 7.81250000e-03\n",
            "  1.25000000e-01 1.95312500e-03]\n",
            " [9.76562500e-04 1.56250000e-02 9.76562500e-04 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01 1.95312500e-03 1.56250000e-02\n",
            "  1.95312500e-03 3.12500000e-02]\n",
            " [1.56250000e-02 9.76562500e-04 1.25000000e-01 9.76562500e-04\n",
            "  2.50000000e-01 2.27373675e-13 1.56250000e-02 3.90625000e-03\n",
            "  6.25000000e-02 7.81250000e-03]\n",
            " [6.25000000e-02 6.25000000e-02 1.95312500e-03 9.76562500e-04\n",
            "  1.95312500e-03 1.56250000e-02 2.27373675e-13 2.50000000e-01\n",
            "  9.76562500e-04 3.90625000e-03]\n",
            " [1.95312500e-03 9.76562500e-04 6.25000000e-02 1.56250000e-02\n",
            "  3.12500000e-02 7.81250000e-03 2.50000000e-01 2.27373675e-13\n",
            "  2.50000000e-01 9.76562500e-04]\n",
            " [9.76562500e-04 6.25000000e-02 9.76562500e-04 2.50000000e-01\n",
            "  1.95312500e-03 6.25000000e-02 1.95312500e-03 2.50000000e-01\n",
            "  2.27373675e-13 2.50000000e-01]\n",
            " [2.50000000e-01 4.88281250e-04 6.25000000e-02 1.95312500e-03\n",
            "  3.12500000e-02 7.81250000e-03 7.81250000e-03 9.76562500e-04\n",
            "  2.50000000e-01 2.27373675e-13]]\n",
            "\n",
            "Final Optimized Antenna Positions:\n",
            "[[2. 8.]\n",
            " [8. 2.]\n",
            " [2. 8.]\n",
            " [0. 2.]\n",
            " [2. 0.]\n",
            " [9. 1.]\n",
            " [0. 2.]\n",
            " [3. 6.]\n",
            " [6. 3.]\n",
            " [3. 6.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.scatter(antenna_positions[:, 0], antenna_positions[:, 1], c='red', marker='o', label='Antenna Positions')\n",
        "plt.scatter(loopback_vibrator[0], loopback_vibrator[1], c='blue', marker='x', label='Loopback Vibrator')\n",
        "plt.scatter(unloopback_vibrator[0], unloopback_vibrator[1], c='green', marker='x', label='Unloopback Vibrator')\n",
        "plt.title('Optimized Antenna Positions')\n",
        "plt.xlabel('X-axis')\n",
        "plt.ylabel('Y-axis')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.imshow(pheromones, cmap='hot', interpolation='nearest')\n",
        "plt.colorbar()\n",
        "plt.title('Pheromone Matrix')\n",
        "plt.xlabel('X-axis')\n",
        "plt.ylabel('Y-axis')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u4N1hr-jlxpy",
        "outputId": "f5bdcac9-c88b-49ef-f7f0-9b49c0a04093"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "num_ants = 10\n",
        "num_iterations = 100\n",
        "pheromone_decay = 0.5\n",
        "alpha = 1.0\n",
        "beta = 2.0\n",
        "grid_size = 10\n",
        "antenna_positions = np.random.rand(num_ants, 2) * grid_size\n",
        "pheromones = np.ones((grid_size, grid_size))\n",
        "loopback_vibrator = np.array([5, 5])\n",
        "unloopback_vibrator = np.array([8, 8])\n",
        "def calculate_heuristic(position):\n",
        "    distances = np.linalg.norm(antenna_positions - position[:, np.newaxis, :], axis=2)\n",
        "    return 1.0 / (distances + 1e-6)\n",
        "convergence_data = []\n",
        "for iteration in range(num_iterations):\n",
        "    for ant in range(num_ants):\n",
        "        current_position = antenna_positions[ant]\n",
        "        heuristic = calculate_heuristic(antenna_positions)\n",
        "        pheromone = pheromones.flatten()\n",
        "        probabilities = (pheromone.reshape((grid_size, grid_size)) ** alpha) * (heuristic ** beta)\n",
        "        current_positions_indices = np.ravel_multi_index(current_position.astype(int).T, (grid_size, grid_size))\n",
        "        probabilities.ravel()[current_positions_indices] = 0\n",
        "        selected_index = np.argmax(probabilities)\n",
        "        selected_position = np.unravel_index(selected_index, (grid_size, grid_size))\n",
        "        pheromones[selected_position] *= (1 - pheromone_decay)\n",
        "        antenna_positions[ant] = selected_position\n",
        "    total_pheromone = np.sum(pheromones)\n",
        "    convergence_data.append(total_pheromone)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(range(1, num_iterations + 1), convergence_data, marker='o', linestyle='-', color='b')\n",
        "plt.title('Convergence Plot')\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Total Pheromone Concentration')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.imshow(pheromones, cmap='Blues', interpolation='nearest')\n",
        "for ant in range(num_ants):\n",
        "    plt.plot(antenna_positions[ant, 1], antenna_positions[ant, 0], marker='o', markersize=8, color='red')\n",
        "plt.title('Ant Movement')\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('Y')\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.scatter(antenna_positions[:, 0], antenna_positions[:, 1], c='red', marker='o', label='Antenna Positions')\n",
        "plt.scatter(loopback_vibrator[0], loopback_vibrator[1], c='blue', marker='x', label='Loopback Vibrator')\n",
        "plt.scatter(unloopback_vibrator[0], unloopback_vibrator[1], c='green', marker='x', label='Unloopback Vibrator')\n",
        "plt.title('Optimized Antenna Positions')\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('Y')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "osRaW6tWmH_X",
        "outputId": "33a4652a-759e-474e-eaf7-f9d24b7c9807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}