A Novel Ant Colony System Based on Delauney Triangulation and Self-adaptive Mutation for TSP

A novel ant colony system which employs a candidate set strategy based on Delaunay triangulation (CSDT) and a self-adaptive mutation operator (SMO) for TSP (DSMACS) is proposed. Under the condition that all the edges in the global optimal tour are nearly all contained in the candidate sets, CSDT can limit the selection scope of ants at each step to average six cities below, and thus substantially reduce the size of search space. To the shortage that search is possibly trapped in local optimal regions owing to the locality of this candidate set, SMO, which can self-adaptively adjust the size of neighborhood search scope of mutation operator, is designed to improve the global search ability of DSMACS by combining inversion and inserting mutation operator in genetic algorithm. The Simulation of TSP shows DSMACS can not only greatly increase the convergence speed but also avoid the premature convergence phenomenon effectively.