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一种具有自适应迁移能力的多粒子群协同优化算法 被引量：4

Cooperative Particle Swarm Optimization Algorithm Based on Adaptive Migratory Operator
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摘要 基于群熵的概念提出了一种具有自适应迁移能力的多种群PSO算法.对2个著名的基准函数进行测试,结果表明:与经典PSO和多种群协同PSO等算法相比,新算法能更有效地控制粒子群的多样性,从而提高了算法的收敛精度,改善了算法的优化性能. This paper presents an adaptive migratory PSCO base on the concept of population entropy.The experiment results conducted on two benchmark functions show the proposed method are superior to traditional cooperative PSO,especially in controlling the diversity of swarm.That is,the new algorithm can effectively avoid local optimum and enhance the performance of global searching.
作者 蔡国榕 陈水利 李绍滋 吴云东
机构地区 厦门大学信息科学与技术学院 集美大学影像信息工程技术研究中心
出处 《厦门大学学报（自然科学版）》 CAS CSCD 北大核心 2010年第6期772-777,共6页 Journal of Xiamen University：Natural Science
基金 国家自然科学基金资助项目(60873179) 高等学校博士学科点专项科研基金项目(20090121110032) 福建省高校服务海西重点项目(闽教高(2009)8号) 福建省自然科学基金资助项目(2008J0024) 福建省教育厅重点项目(JK2009017),福建省教育厅项目(JA10196) 厦门市科技计划项目(3502Z20093018)
关键词 粒子群优化 迁移算子 种群熵 particle swarm optimization migration operator population entropy
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献13

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二级参考文献29

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共引文献452

同被引文献40

  • 1史有涛.论结构优化设计[J].建筑科学,2006,22(3):74-77. 被引量：9
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  • 3Fei Ye,Yi-Na Li. Group Multi-Attribute Decision Model to Partner Selection in the Formation of Virtual Enterprise under Incomplete Information[J]. Expert Systems with Applications, 2009,36(5) : 9350- 9357.
  • 4Jungtae Mun,Moonsoo Shin,Kyunghuy Lee,Mooyoung Jung. Manufacturing Enterprise Collaboration Based on a Goal-Oriented Fuzzy Trust Evaluation Model in a Virtual Enterprise [J]. Computer & Industrial Engineering, 2009,56(3 ) : 888-901.
  • 5Rajesh Kumar,Debendra Sharma,Abhinav Sadu. A Hybrid Multi-Agent Based Particle Swarm Optimizstion Algorithm for Economic Power Dispatch [J]. Electrical Power and Energy Systems,2011,33 ( 1 ) : 115- 123.
  • 6R J Kuo,S Y Hong,Y C Huang. Integration of Particle Swarm Optimization-Based Fuzzy Neural Network and Artificial Neural Network for Supplier Selection [J].Applied Mathematical Modelling,2020,34 ( 12 ) : 3976-3990.
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引证文献4

二级引证文献26

厦门大学学报（自然科学版）
2010年 第6期

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