An effective traveling Salesman Problem solver based on Self-Organizing Map

Abstract

Combinatorial optimization seems to be a harsh field for Artificial Neural Networks (ANN), and in particular the Traveling Salesman Problem (TSP) is an exemplar benchmark where ANN today are not competitive with the best heuristics from the operations research literature. The thesis upheld in this work is that the Self-Organizing feature Map (SOM) paradigm can be an effective solving method for the TSP, if combined with appropriate mechanisms improving the efficiency and the accuracy. An original TSP-solver based on the SOM is tested over the largest TSP benchmarks, on which other ANN typically fail. © Springer-Verlag Berlin Heidelberg 2002.