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结合历史全局最优与局部最优的粒子群算法 被引量：5

A Combine Historical Global and Local Best Particle Swarm Optimization Algorithm
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摘要 提出了一种增加粒子共享信息多样性的粒子群算法。该算法在粒子更新速度的过程中,将前几轮粒子搜索的历史全局最优信息与本轮局部最优粒子信息结合,增加粒子搜索信息的多样性。另外,根据2种信息的结合方式不同,将基本算法扩展成3种扩展型算法。6个典型函数的仿真实验结果说明,改进的粒子群算法可以有效地克服粒子群算法中的早熟现象。 A new particle swarm optimization algorithm was proposed to increase the diversity of the shared information.In the process of velocity updating,the historical global best in the previous rounds was combined with the local best in the current round to increase the diversity of information.In addition,according to the different combining ways of two kinds of information,the basic algorithm was extended to 3 kinds of extension algorithm.Simulation results on 6 typical functions showed that the improved particle swarm algorithm can efficiently overcome the premature of standard particle swarm algorithm.
作者 闫雪丽 王学武 连志刚
机构地区 华东理工大学信息科学与工程学院 上海电机学院 江南造船(集团)有限责任公司
出处 《华东理工大学学报（自然科学版）》 CAS CSCD 北大核心 2011年第4期515-520,共6页 Journal of East China University of Science and Technology
基金 教育部人文社会科学研究青年基金项目(09yjc630151) 上海高校选拔培养优秀青年教师科研专项基金(sdju200903) 2011年上海市博士后科研资助计划项目(11R21420100) 上海市科委创新项目(11YZ268) 中国博士后科学基金面上资助项目(20110490729)
关键词 共享 多样性 历史全局最优 局部最优 早熟现象 shared diversity historical global best local best premature
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献15

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二级参考文献9

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共引文献10

同被引文献40

  • 1李宁,邹彤,孙德宝.带时间窗车辆路径问题的粒子群算法[J].系统工程理论与实践,2004,24(4):130-135. 被引量：60
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引证文献5

二级引证文献12

华东理工大学学报（自然科学版）
2011年 第4期

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