计算机工程与科学2011，Vol.33Issue(5)：91-96,6.DOI:10.3969/j.issn.1007-130X.2011.05.018
基于动态种群和广义学习的粒子群算法及应用
A Particle Swarm Optimizer and Its Application Based on Dynamic Population and Comprehensive Learning
摘要
Abstract
In order to improve the ability to escape from local optima, we present an improved particle swarm optimizer based on dynamic population and comprehensive learning (DCPSO for short). In DCPSO, the swarm population growing and declining strategies are introduced to increase the swarm diversity, further improve the ability to escape from local optima; a comprehensive learning strategy also is used to improve the probability of flying to the global best position. In the benchmark function, the results demonstrate good performance of the DCPSO algorithm in solving complex multimodal problems when compared with other PSO variants. In the optimization design for the box grider of portal gantry,the experimental results show that the DCPSO algorithm can achieve better solutions that other PSOs.关键词
动态种群/广义学习/粒子群算法Key words
dynamic population / comprehensive learning/ particle swarm optimizer分类
信息技术与安全科学引用本文复制引用
刘衍民,赵庆祯..基于动态种群和广义学习的粒子群算法及应用[J].计算机工程与科学,2011,33(5):91-96,6.基金项目
山东省科技攻关项目(2009GGl0001008) （2009GGl0001008）
贵州教育厅社科项目(0705204) （0705204）
