Opposition-based Particle Swarm Algorithm with Cauchy mutation

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 an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and avoid premature convergence. The proposed method employs opposition-based learning for each particle and applies a dynamic Cauchy mutation on the best particle. Experimental results on many well-known benchmark optimization problems have shown that OPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization.