关键词: 粒子群算法, 追尾行为, 自适应算子, 变异算子

Abstract: In order to avoid the premature convergence and shock of traditional particle swarm optimization algorithm，a novel adaptive mutation algorithm with following behavior is proposed.If paritcles’ density in the visual region added around optimal particle is higher than the given criteria， their individual extreme value points will be adaptively mutated in given probability.Then consider whether they are updated when comparing with corrent ones.The mutation operation can’t be stopped until it reaches the given mutation step，particles fly away the visual region or update the global extreme value point.The algorithm not only improves searching ability but also reduces computational cost.Simulation with testing function demonstrates that the novel mothod is effective and efficient.

Key words: Particle Swarm Optimization（PSO）, following behavior, adaptive operator, mutation operator