Chaotic particle swarm optimization

Particle Swarm Optimization (PSO) is an efficient, simple and fertile Optimization Algorithm. However, it suffers from premature convergence; moreover, the performance of PSO depends significantly on its parameters settings. PSO attracts attention from researchers; they try to improve algorithm performance and avoid its weakness. In this paper, we propose a new methodology that uses chaotic agents to search in promising areas that are explored by PSO. The results proved that this method enhances the search efficiency significantly and improve the search quality.