Enhanced Self-Organizing Map Solution for the Traveling Salesman Problem

Abstract

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.

@inproceedings{dantas2021enhanced,
  author = {Dantas, Joao P. A. and Costa, Andre N. and Maximo, Marcos R. O. A. and Yoneyama, Takashi},
  title = {{Enhanced Self-Organizing Map Solution for the Traveling Salesman Problem}},
  booktitle = {Anais do XVIII Encontro Nacional de Intelig\^{e}ncia Artificial e Computacional (ENIAC)},
  year = {2021},
  pages = {799--802},
  publisher = {Sociedade Brasileira de Computa\c{c}\~{a}o (SBC)},
  doi = {10.5753/eniac.2021.18428},
  url = {https://doi.org/10.5753/eniac.2021.18428}
}