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
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.
中图分类号:
杨燕,靳蕃,Kamel M. 微粒群优化算法研究现状及其进展[J]. , 2004, 30(21): 7-.
YANG Yan1, JIN Fan1, Kamel M2. Research and Development of Particle Swarm Optimization[J]. , 2004, 30(21): 7-.