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多目标微粒群优化算法及其应用研究进展 被引量:5

Multi-objective particle swarm optimization algorithm and its application
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摘要 针对近几年来MOPSO算法及其应用的进展进行了综述和评论。首先描述了MOPSO算法的基本框架;接着对MOPSO算法进行了分类和分析,并给出了MOPSO算法的一些改进策略;然后介绍了MOPSO算法的应用进展;最后展望了MOPSO算法值得进一步研究的方向。 This paper aimed at providing a review and discussion of MOPSO algorithm and its application in the recent years.Firstly,described the basic framework of MOPSO algorithm briefly.Then,proposed and analyzed a taxonomy for MOPSO algorithms,and also presented some effective strategies,which could improve the performance of MOPSO algorithms.Thereafter,introduced some typical applications of MOPSO algorithms.Finally,pointed out some promising directions for future research in this field.
出处 《计算机应用研究》 CSCD 北大核心 2011年第4期1225-1231,共7页 Application Research of Computers
基金 江西省自然科学基金资助项目(2009GQS0062)
关键词 多目标优化 多目标微粒群优化 算法 应用 multi-objective optimization multi-objective particle swarm optimization(MOPSO) algorithm application
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  • 1KENNEDY J, EBERHART R C. Particle swarm optimization [ C ]// Prnc of IEEE International Conference on Neural Networks. [ S, 1.] : IEEE Service Center, 1995 : 1942-1948.
  • 2JACQUELINE M, RICHARD C. Application of particle swarm to multiobjective optimization [ D ]. Auburn : Department of Computer Science and Software Engineering, Auburn University, 1999.
  • 3SIERRA M R, COELLO C A C. Multi-objective particle swarm optimizers : a survey of the state-of-the-art[J]. tnternationat Journat of Computational Intelligence Research,2006,2(3) :287-308.
  • 4郑向伟 刘弘.一种基于合作型协同和s-占优的多目标微粒群算法.软件学报,:109-119.
  • 5PARSOPOULOS K E, VRAHATIS M N. Particle swarm optimization method in muhiobjective problems[ C ]//Proc of ACM Symposium on Applied Computing. Madrid: Association for Computing Machinery Press, 2002 : 603 - 607.
  • 6HU Xiao-hui, EBERHART R C. Multiobjective optimization using dynamic neighborhood particle swami optimization [ C ]//Proc of IEEE Congress on Evolutionary Computation. [ S, I.] : IEEE Press, 2002: 1677-1681.
  • 7HUANG V L, SUGANTHAN P N, LIANG J J. Comprehensive learning particle swarm optimizer for solving muhiobjeetive optimization problems [ J ]. International Journal of Intelligent Systems, 2006,21 (2) :209-226.
  • 8雷德明,吴智铭.Pareto档案多目标粒子群优化[J].模式识别与人工智能,2006,19(4):475-480. 被引量:26
  • 9HO S L,YANG Shi-you,Nl Guang-zheng, et al. A particle swarm optimization-based method for multiobjective design optimizations [ J ]. IEEE Trans on Magnetics ,2005,41 (5) :1756-1759.
  • 10ALVAREZ-BENITEZ J, EVERSON R, FIELDSEND J. A MOPSO algorithm based cxclusively on Pareto dominance concepts[ C ]//Proc of the 3rd International Conference on Evolutionary Multi-Criterion Optimization. [ S. I.] : Springer-Verlag,2005:459-473.

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