陕西科技大学学报（自然科学版）2011，Vol.29Issue(5)：140-144,5.
一种基于阶段进化适应性策略的粒子群算法
A PARTICLE SWARM ALGORITHM BASED ON STAGED EVOLUTION ADAPTATION STRATEGIES
摘要
Abstract
This paper studies the particle swarm algorithm for solving multimodal function optimization. To overcome the drawback of easily trapping in local optimum, we propose an improved strategy, denoted FPSO. In this strategy, evolution process is divided into three stages, and different group size and inertia are used in each stage. Furthermore, to enhance the ability of jumping out of local optimum, different mutations are introduced into the first and third stage, respectively. The results of simulations for different benchmark functions illustrate that new algorithm improves clearly the global search capability, and the ability of jumping out of local optimum and convergence speed are superior to that of the standard particle swarm optimization.关键词
粒子群算法/阶段进化/变异/早熟收敛Key words
particle swarm optimization/ staged evolution/ mutation/ premature convergence分类
计算机与自动化引用本文复制引用
李娥,高兴宝..一种基于阶段进化适应性策略的粒子群算法[J].陕西科技大学学报（自然科学版）,2011,29(5):140-144,5.基金项目
国家自然科学基金资助项目(10902062 （）
60671063) （）
