关键词: 粒子群, 自适应变异, 优化, 早熟收敛

Abstract: A new adaptive mutation particle swarm optimizer(AMPSO),which is based on the variance of the population's fitness is presented.During the running time,the mutation probability for the current best particle is determined by two factors:the variance of the population's fitness and the current optimal solution.The ability of particle swarm optimization algorithm(PSO) to break away from the local optimum is greatly improved by the mutation.The experimental results show that the new algorithm not only has great advantage of convergence property over genetic algorithm and PSO,but also can avoid the premature convergence problem effectively.

Key words: particle swarm, adaptive mutation, optimization, premature convergence