Particle Swarm Optimization with Adaptive Mutation
Jun Tang, Xiaojuan Zhao · 2009
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.