A memetic neural network for the Euclidean traveling salesman problem

We study the hybridization of the self-organizing map (SOM) in an evolutionary algorithm to solve the Euclidean traveling salesman problem (TSP). The evolutionary dynamics consist of interleaving the SOM execution with a mapping operator, a fitness evaluation and a selection operator. SOM and mapping operators have a similar structure based on closest point findings and simple moves performed in the plane. We evaluate the approach on standard TSP test problems and show that it performs better, with respect to solution quality and/or computation time, than other neural network approaches given previously in the literature. Experiments are conducted on 91 publicly available TSP instances with up to 85 900 cities.