TITLE: Enhanced Self-Organizing Map Solution for the Traveling Salesman Problem

AUTHORS: Joao P. A. Dantas, Andre N. Costa, Marcos R. O. A. Maximo, Takashi Yoneyama

Using an enhanced Self-Organizing Map method, we provided suboptimal solutions to the Traveling Salesman Problem. Besides, we employed hyperparameter tuning to identify the most critical features in the algorithm. All improvements in the benchmark work brought consistent results and may inspire future efforts to improve this algorithm and apply it to different problems.

This code was used to get 3rd place in the 5th Brazilian Competition on Knowledge Discovery in Databases (KDD-BR).

Please check the full paper if you need any additional information.

You will need Python 3 and R and their dependencies to run the codes. Please check the two Jupyter Notebooks that are in the code folder. The images generated will be stored in the diagrams folder.

Citation

@article{dantas2021enhanced,
  title={Enhanced Self-Organizing Map Solution for the Traveling Salesman Problem},
  author={Dantas, Joao P. A. and Costa, Andre N. and Maximo, Marcos R. O. A. and Yoneyama, Takashi},
  journal={arXiv preprint arXiv:2201.07208},
  year={2021}
}