关键词: 粒子熵集, 惯性权重, 全局最优位置, 自适应变异, 粒子群优化算法

Abstract: A new Particle Swarm Optimization（PSO） algorithm with global optimization of parameters for adaptive mutation is proposed around two key parameters[w]and[pgd]which all affect PSO algorithm performance to avoid the possible problems about local convergence and low precision. The concept of particle entropy set is defined which can accurately reflect the PSO data global aggregation behavior. The regression variance for inertia weight[w]of swarm dimensional data and the random variance for global variable[pgd]are determined by the particle entropy of every dimension data, and the method of using mutation frequency factor is used to avoid divergence in the algorithm. Simulation results show that compared with the conventional algorithm there are great advantages in optimization precision, getting rid of local traps, stability, etc, and good performance in solving complex multimodal problems with this algorithm.

Key words: particle entropy set, inertia weight, global optimal location, adaptive mutation, Particle Swarm Optimization（PSO）