An Improved Particle Swarm Optimization Algorithm Based on Cauchy Operator and 3-Opt for TSP

An improved particle swarm optimization (PSO) algorithm based on self-adaptive excellence coefficients, Cauchy operator and 3-opt, called SCLPSO, is proposed in this paper in order to deal with the issues such as premature convergence and low accuracy of the basic discrete PSO when applied to traveling salesman problem (TSP). To improve the optimization ability and convergence speed of the algorithm, each edge is assigned a self-adaptive excellence coefficient based on the principle of roulette selection, which can be adjusted dynamically according to the process of searching for the solutions. To gain better global search ability of the basic discrete PSO, the Cauchy distribution density function is used to regulate the inertia weight so as to improve the diversity of the population. Furthermore, the 3-opt local search technique is utilized to increase the accuracy and convergence speed of the algorithm. Through simulation experiments with MATLAB, the performance of the proposed algorithm is evaluated on several classical examples taken from the TSPLIB. The experimental results indicate that the proposed SCLPSO algorithm performs better in terms of accuracy and convergence speed compared with several other algorithms, and thus is a potential intelligence algorithm for solving TSP.