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 。
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.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2004年第3期416-420,共5页
Acta Electronica Sinica
基金
甘肃省自然科学基金项目 (No .ZS0 1 1 A2 5 0 1 6 G)
