Particle Swarm Optimization with Adaptive Mutation

Particle swarm optimization (PSO) has shown its good performance in many optimization problems. However, PSO could often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO (AMPSO) to solve this problem by applying a novel adaptive mutation operator. Experimental results on 8 well-known benchmark functions show that the AMPSO achieves better results than the standard PSO, PSO with Gaussian mutation and PSO with Cauchy mutation on most test cases.