An Improved Discrete Particle Swarm Optimization Algorithm for TSP

Abstract

An Improved discrete particle swarm optimization (DPSO)-based algorithm for the traveling salesman problem (TSP) is proposed. In order to overcome the problem of premature convergence, a novel depressor is proposed and a diversity measure to control the swarm is also introduced which can be used to switch between the attractor and depressor. The proposed algorithm has been applied to a set of benchmark problems and compared with the existing algorithms for solving TSP using swarm intelligence. The results show that it can prevent premature convergence to a high degree, but still keeps a rapid convergence like the basic DPSO.