| 微粒群优化算法研究现状及其进展 |
| |
| 引用本文： | 杨燕,靳蕃,Kamel M. 微粒群优化算法研究现状及其进展[J]. 计算机工程, 2004, 30(21): 3-4,9 |
| |
| 作者姓名： | 杨燕 靳蕃 Kamel M |
| |
| 作者单位： | 西南交通大学计算机与通信工程学院,成都,610031;University of Waterloo,Waterloo,Ontario,Canada,N2L 3GI |
| |
| 摘 要： | 对进化计算中引起广泛兴趣的微粒群优化(PSO)算法的研究现状进行了考察，介绍了一些最新研究进展，包括：杂交PSO、基于邻域算子的PSO和基于不同搜索方向的PSO，并简要介绍了PSO在求解复杂优化问题如多目标优化和带约束优化中的优势。最后给出了一些应用实例，讨论了将来可能的研究内容。
|
| 关 键 词： | 微粒群优化 进化计算 群集智能 |
| 文章编号： | 1000-3428(2004)21-0003-02 |
Research and Development of Particle Swarm Optimization |
| |
| YANG Yan,JIN Fan,Kamel M. Research and Development of Particle Swarm Optimization[J]. Computer Engineering, 2004, 30(21): 3-4,9 |
| |
| Authors: | YANG Yan JIN Fan Kamel M |
| |
| Affiliation: | YANG Yan1,JIN Fan1,Kamel M2 |
| |
| Abstract: | This paper reviews the research of particle swarm optimization (PSO), which is an interesting branch in evolutionary computation. Some latest research results are presented: hybrid PSO, PSO with neighborhood operator, and PSO based on different search directions. The paper also describes the advantages of PSO in solving complicated optimization problems such as multi-objective particle swarm optimization and constrained optimization. Finally a few applications in engineering are introduced, and the future research issues are discussed. |
| |
| Keywords: | Particle swarm optimization Evolutionary computation Swarm intelligence |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
