离散微粒群优化算法的研究进展  

The-state-of-art of discrete particle swarm optimization algorithms

作　　者：潘全科[1] 王凌[2] 高亮[3] 

机构地区：[1]聊城大学计算机学院,山东聊城252059 [2]清华大学自动化系,北京100084 [3]华中科技大学工业及制造系统工程系,武汉430074

出　　处：《控制与决策》2009年第10期1441-1449,共9页Control and Decision

基　　金：国家自然科学基金项目(60874075;70871065;60774082;60834004);国家863计划项目(2007AA04Z155);数字制造装备与技术国家重点实验室(华中科技大学)开放课题

摘　　要：首先,介绍了近年来出现的5种较为典型的离散PSO,并分析了它们与基本PSO之间的联系和区别;然后,归纳了提高离散PSO优化性能的若干途径,并总结了离散PSO的应用现状;最后,探讨了离散PSO有待进一步研究的若干方向和内容.Five kinds of representative discrete particle swarm optimization（PSO） algorithms presented in recent years are introduced in this paper. And the relation and distinction between the discrete PSO and the basic PSO are analyzed. Then several methods to improve the discrete PSO algorithms are comprehensively analyzed and concluded. Also, the state of art in the application of the discrete PSO algorithm is investigated in detail. Finally, further research issues and some suggestion about the discrete PSO algorithm in future are discussed.

关 键 词：微粒群优化 离散微粒群优化 进化计算 群智能 组合优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]