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基于种群熵的多粒子群协同优化 被引量：2

Co-evolutionary particle swarm optimization based on population entropy
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摘要 提出了一种基于种群熵的多粒子群协同优化算法,通过引入熵对种群粒子的分布性进行度量,然后利用它来引导在多种群协同演化中粒子迁徙的时间和方向,从而保持粒子在寻优过程中的多样性和快速性。通过四个典型测试函数的仿真说明了该算法具有摆脱局部极值能力和较高的收敛速度。 This paper applied a modified CPSO based on population entropy in the context of ECPSO. The entropy was used to measure the diversity of the whole population and then guided the particles how to migrate. The ECPSO was tested on some benchmark optimization problems and the results show a superior performance compared with the standard PSO and CPSO.
作者 胡成玉 吴湘宁 王永骥
机构地区 华中科技大学控制科学与工程系 中国地质大学计算机学院
出处 《计算机应用研究》 CSCD 北大核心 2008年第12期3593-3595,共3页 Application Research of Computers
基金 国家自然科学基金资助项目(60674105) 中国地质大学(武汉)优秀青年教师计划资助项目(CUGQNL0821)
关键词 种群熵 粒子群优化 协同 population entropy particle swarm optimization（PSO） co-evolutionary
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献13

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共引文献415

同被引文献22

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计算机应用研究
2008年 第12期

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