An Improved Particle Swarm Optimization for Traveling Salesman Problem

This paper deals with the traveling salesman problem with the particle swarm optimization algorithm.To overcome the disadvantages of premature convergence and stagnation phenomenon of the standard particle swarm optimization algorithm,this paper proposes an improved particle swarm optimization algorithm for the traveling salesman problem.Firstly,in the selection of an initial population,a modified greedy strategy is exploited to directly obtain a population of high-performance initial solutions so as to improve the search efficiency of the algorithm.Secondly,through introducing sub-optimal attractor,the particles in the search process can make full use of the population information to enhance their own performance,so as to effectively inhibit stagnation in the convergence process,and improve the search ability of the algorithm.Finally,in order to verify the effectiveness and feasibility of the proposed method,the instances in the standard library TSPLIB have been tested and the numerical results are given.