An Improved Particle Swarm Optimization Algorithm for Traveling Salesman Problems

Abstract

Particle Swarm Optimization algorithm (PSO) is a meta-heuristic algorithm. It makes few or no assumptions about the problem being optimized, and can search a very large space of candidate solutions. However, it does not guarantee to find an optimal solution. In this paper with the guidance of the analysis of the advantages and disadvantages of the standard PSO, we propose a novel Particle Swarm Optimization algorithm, which introduces an extra mechanism for sharing information and a competition strategy. The proposed algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Our experimental results show it performs much better than the standard PSO on benchmark functions, especially for difficult functions. We also apply it to solve the traveling salesman problems (TSP). It significantly improves the success rate of finding the optimal solutions.