Re-diversification Based Particle Swarm Algorithm with Cauchy Mutation

摘要

Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima. This paper presents a hybrid PSO algorithm called RPSO by applying a new re-diversification mechanism and a dynamic Cauchy mutation operator to accelerate the convergence of PSO and avoid premature convergence. Experimental results on many well-known benchmark optimization problems have shown that the RPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization.