Study on Immune PSO Hybrid Optimization Algorithm

Abstract

Particle swarm optimization (PSO) has poor diversity, slow convergence speed and is easy to trap into local optimum during the course of searching, a modified particle swarm optimization algorithm based on immune mechanism is proposed. The new algorithm has both the properties of the original particle swarm optimization algorithm and the immune diversity keeping mechanism, and can improve the abilities of seeking the global optimum and evolution speed. The simulation results of multi-modal function optimization show that the proposed algorithm can inhibit premature effectively and has preferable global convergent performance.