TACO

Travelling salesman problem (TSP) using the Ant Colony Optimization (ACO) algorithm.

Note: the starting node/city is always random, but the path, as well as the objective distance value, are always the same.

How to Install

After you download the project's directory, you may install it running the following command:

python -m pip install -e taco

Or, if you happen to be inside of the project's directory:

python -m pip install -e .

How to Run

Once the project is installed, you can run it from anywhere with:

python -m taco

This will use the default dataset that exists in the following path: taco/data/tsp_data.csv

Note: you can run the module without installing it, but it is important to make sure that all dependencies are satisfied, such as Numpy. If desired, you can install all required dependencies in the requirements.txt file with the following command:

python -m pip install -r requirements.txt

Finally, if you would like to use a different example, you can either provide your own .csv file that contains a distance matrix in the expected format, or select any from the data directory. If you find one that you like, you can run it as follows:

python -m taco taco/data/google_or_tools.csv

How to Test

If you have a lot of time and would like to run all of the available examples in the data directory, you can do so as follows:

python -m unittest -v

You may also run a specific test-case of interest, such as the following:

python -m unittest -v tests.test_data.TestData.test_tsp_data

Resulting objective values from all examples have been verified using the optimal solutions for symmetric TSPs.

Disclaimer

All credits go to TSPLIB group and the respective authors for each example that I used to test my software.