Research toolkit

This repository contains two auxiliary methods that compound my master's research application: a Tabu Search and a Brute Force algorithms, both applied to the electric network reconfiguration problem.

Dependencies

This repository depends on some other Python modules. Below I listed these dependencies:

• Pandapower;
• Color lib;
• Graph (in the adjacency matrix form);

To run the features of this repository, install the modules above in your Python env.

Tabu Search

The Tabu Search (TS) is modeled according the constraints and criterias defined in my master's research. To know how they work, refer to my master's thesis (Brazilian portuguese only).

Running the Tabu Search

After installed the dependencies, run the TS on two ways:

1. Importing it in a Python shell:

import ts
tl.run(loops = 10)

2. Running the file directly trought the Linux shell:

python ts.py

Running the TS throught the Linux shell, the method will run 10 times and it will show the results at the end. If you want to run the TS for a less times, import it in a Python shell and set the loops parameter in when calling the run(int) method.

To set the parameters of the search, see the initial variables of the code in the ts.py file. All the parameters of the search are respectively comemented.

Brute Force algorithm

The Brute Force (BF) algorithm aims to list the optimal solutions of the choosed scenario. To keenly know how it works, refer to my master's thesis (Brazilian portuguese only).

Running the Brute Force algorithm

After installed the dependencies, run the BF on two ways:

1. Importing it in a Python shell:

import bf
bf.run()

2. Running the file directly trought the Linux shell:

python bf.py

To set the parameters of the search, see the initial variables of the code in the bf.py file. All the parameters of the search are respectively comemented.