An improved genetic algorithm for multiple traveling salesman problem

Abstract

Multiple traveling salesman problem, which uses the shortest total route as an optimization criteria, has huge application in both theoretical research and industry. This paper presents an improved genetic algorithm to provide an alternative and effective solution to the problem. The initial population was generated by greedy strategy, this enabled selected sub-route to be included in the initial population. Convergent speed was increased and at the same time complexity was significantly reduced. The mutation operator combined with 2-opt local search algorithm was used to avoid the limitation of local search ability of genetic algorithm. It also solved the problems of the simple genetic algorithm such as premature phenomena and slow convergence. The simulation results based on our algorithm show that the improved method is effective and feasible.