https://github.com/diego-vicente/som-tsp

Solving the Traveling Salesman Problem using Self-Organizing Maps
https://github.com/diego-vicente/som-tsp

self-organizing-map som traveling-salesman tsp tsp-approximation tsp-problem

Last synced: 7 days ago
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Solving the Traveling Salesman Problem using Self-Organizing Maps

• Host: GitHub
• URL: https://github.com/diego-vicente/som-tsp
• Owner: diego-vicente
• License: mit
• Created: 2017-11-06T20:30:12.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-12-24T16:40:59.000Z (12 months ago)
• Last Synced: 2024-11-20T21:56:43.194Z (22 days ago)
• Topics: self-organizing-map, som, traveling-salesman, tsp, tsp-approximation, tsp-problem
• Language: Python
• Homepage: https://diego.codes/post/som-tsp/
• Size: 5.23 MB
• Stars: 3,867
• Watchers: 117
• Forks: 605
• Open Issues: 0
• Metadata Files:
  • Readme: README.org
  • License: LICENSE

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• awesome-ccamel - diego-vicente/som-tsp - Solving the Traveling Salesman Problem using Self-Organizing Maps (Python)

README

        
#+TITLE:  Solving the Traveling Salesman Problem using Self-Organizing Maps

#+AUTHOR: Diego Vicente Martín

#+EMAIL:  [email protected]

This repository contains an implementation of a Self Organizing Map that can be

used to find sub-optimal solutions for the Traveling Salesman Problem. The

instances of the problems that the program supports are =.tsp= files, which is

a widespread format in this problem. All the source code can be found in the

=src= directory, while a report and brief presentation slides (in Spanish) can

be found in the =report= folder. However, for a complete read on the topic, you

can read [[https://diego.codes/post/som-tsp/][my blog post explaining this implementation and its evaluation]].

[[file:diagrams/uruguay.gif]]

To run the code, only Python 3 and the dependencies (=matplotlib=, =numpy= and =pandas=,

which are included in the Anaconda distribution by default) are needed. In case

you are not using Anaconda, you can install all the dependencies with:

#+BEGIN_SRC sh

pip install -r requirements.txt

#+END_SRC

To run the code, simply execute:

#+BEGIN_SRC sh

cd som-tsp

python src/main.py assets/.tsp

#+END_SRC

The images generated will be stored in the =diagrams= folder. Using a tool like

=convert=, you can easily generate an animation like the one in this file by

running:

#+BEGIN_SRC sh

convert -delay 10 -loop 0 *.png animation.gif

#+END_SRC

This code is licensed under MIT License, so feel free to modify and/or use it

in your projects. If you have any doubts, feel free to contact me or contribute

to this repository by creating an issue.

-----

This code was presented for the Bio-Inspired Artificial Intelligence course in

the Computer Science & Technology master's degree @ UC3M. A previous version of

this code can be found in [[https://github.com/DiegoVicen/ntnu-som][this repository]]. Special thanks to [[https://github.com/leo-labs][Leonard Kleinans]],

who worked with me in that previous version.