电子学报 ›› 2004, Vol. 32 ›› Issue (3): 416-420.

• 论文 • 上一篇    下一篇

自适应变异的粒子群优化算法

吕振肃, 侯志荣   

  1. 兰州大学信息科学与工程学院,甘肃兰州 730000
  • 收稿日期:2003-02-20 修回日期:2003-05-28 出版日期:2004-03-25 发布日期:2004-03-25

Particle Swarm Optimization with Adaptive Mutation L Zhen-su,HOU Zhi-rong

  1. School of Information Science and Engineering,Lanzhou University,Lanzhou,Gansu 730000,China
  • Received:2003-02-20 Revised:2003-05-28 Online:2004-03-25 Published:2004-03-25

摘要: 本文提出了一种新的基于群体适应度方差自适应变异的粒子群优化算法(AMPSO).该算法在运行过程中根据群体适应度方差以及当前最优解的大小来确定当前最佳粒子的变异概率,变异操作增强了粒子群优化算法跳出局部最优解的能力.对几种典型函数的测试结果表明:新算法的全局收搜索能力有了显著提高,并且能够有效避免早熟收敛问题.

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

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