关键词: 粒子群（PSO）, 群评价, 变异

Abstract: Particle swarm optimization is an effective random and holistic optimization algorithm，but the classical PSO algorithm easily plunges into local minimums.The paper proposes a new PSO algorithm which uses mutation and self-adjustable parameters.Via introducing the particle swarm evaluation，all the parameters of PSO algorithm can be dynamically adjusted by the evaluation of particle swarm’s holistic capability，then it can search fast in the prophase.At the same time the optimized result found in the particles mutated with the dynamic adjustable probability ensures the multiformity of particles，so it can prevent the algorithm plunging into the local minimums.The experimentative result of the three common testing functions shows the validity of the algorithm.

Key words: Particle Swarm Optimization（PSO）, swarm evaluation, mutation