A new population-based incremental learning method for the traveling salesman problem

Abstract

In this paper binary population-based incremental learning is extended to an integer form, and a new approach to the traveling salesman problem (TSP) is proposed based on linkage relations between cities. The properties of this method are distributed stochastic tour construction, probability distribution initialization, accelerated search based on some power of probability, decision of entropy of probability distribution for terminate condition on process of evolution, and improvement of population solutions using 2-opt/3-opt. Thirteen TSP problems are solved, including ten international contest problems on symmetric and asymmetric TSP problems. The results show that the method proposed in this paper is comparable to the international advanced level on TSP problems and is capable of finding high quality solutions to TSP problems in short times, particularly for large TSP instances.