slime_mold.py

import numpy as np
import scipy.sparse as sparse
import networkx as nx
from queue import PriorityQueue

# Initialize the network
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0)])

# Initialize the parameters
N = 10  # number of plasmodium agents
evaporation_rate = 0.1  # rate of slime evaporation
# concentration of slime at each node
slime_concentration = sparse.csr_matrix((1, len(G.nodes)))

# Initialize the plasmodium agent
plasmodium = np.zeros((len(G.nodes)))

# Generate start and end nodes
start_node = np.random.choice(list(G.nodes))
end_node = np.random.choice(list(G.nodes))

# Propagate the plasmodium agent along the network using a priority queue
queue = PriorityQueue()
queue.put((-plasmodium[start_node], start_node))
while not queue.empty():
    current_node = queue.get()[1]
    if current_node == end_node:
        break
    neighbors = list(G.neighbors(current_node))
    if len(neighbors) == 0:
        break
    for neighbor in neighbors:
        next_concentration = plasmodium[current_node] / len(neighbors)
        if next_concentration > slime_concentration[0, neighbor]:
            slime_concentration[0, neighbor] = next_concentration
            queue.put((-next_concentration, neighbor))
        plasmodium[neighbor] += plasmodium[current_node] / len(neighbors)

    # Update the slime concentration at each node
    slime_concentration *= (1 - evaporation_rate)

# Find the shortest path between two nodes based on the slime concentration
path = [start_node]
while path[-1] != end_node:
    neighbors = list(G.neighbors(path[-1]))
    next_node = neighbors[np.argmax(slime_concentration[0, neighbors])]
    path.append(next_node)

# Print the shortest path
print(path)