计算机工程Issue(10)：203-207,5.DOI:10.3969/j.issn.1000-3428.2013.10.043
一种自适应动态控制参数的粒子群优化算法
A Particle Swarm Optimization Algorithm of Adaptive Dynamic Control Parameter
摘要
Abstract
In the standard Particle Swarm Optimization(PSO), the premature convergence and slow searching of particles decrease the optimization ability of the algorithm. By analyzing global and local search ability, a new adaptive PSO algorithm of dynamic control parameters is proposed. It changes the parameter’s value of learning factor and Inertia weight by particle’s fitness to enhance particle’s search ability. Compared with the standard PSO, experimental result of some typical testing functions proves that the new algorithm has a higher convergence efficiency and faster search speed.关键词
粒子群优化算法/粒子适用度/学习因子/惯性权重/局部搜索能力/全局搜索能力Key words
Particle Swarm Optimization(PSO) algorithm/particle fitness/learning factor/inertia weight/local searching ability/global searching ability分类
信息技术与安全科学引用本文复制引用
徐从东,陈春..一种自适应动态控制参数的粒子群优化算法[J].计算机工程,2013,(10):203-207,5.基金项目
安徽省自然科学基金资助项目(11040606M130) （11040606M130）
