A Modified Particle Swarm Optimization with Adaptive Selection Operator and Mutation Operator

In order to overcome the drawback of classical particle swarm optimization (PSO) such as being subject to being poor in performance of precision and falling into local optimization, a modified PSO is proposed by inducing adaptive mutation operator and selection operator of in PSO. The selection operator of Genetic Algorithms (GA) can improve the fitness of the particle swarm to enhance the searching ability of arithmetic in local. The mutation operator of GA can enlarge the searching scope to avoid premature convergence. The particle swarm will fly to the most optimization by adaptively adjusting the selection operator and mutation operator according to the change of the fitness of the global best particle. The experiment results for typical functions show that the modified PSO can improve the performance of precision and avoid the premature convergence.