东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2008, Vol. 29 ›› Issue (5): 649-652.DOI: -

• 论著 • 上一篇    下一篇

面向高维度目标函数的微粒群优化算法

赵海;宋纯贺;祁田宇;龚红艳;   

  1. 东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2008-05-15 发布日期:2013-06-22
  • 通讯作者: Zhao, H.
  • 作者简介:-
  • 基金资助:
    国家火炬计划项目(2002EB010154)

Study on higher dimensional object function of PSO

Zhao, Hai (1); Song, Chun-He (1); Qi, Tian-Yu (1); Gong, Hong-Yan (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2008-05-15 Published:2013-06-22
  • Contact: Zhao, H.
  • About author:-
  • Supported by:
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摘要： 针对基本微粒群算法在处理高维度目标函数容易出现早熟的问题,提出了一种新的微粒群算法面向高维度目标函数的微粒群算法(HDOF-PSO).分析了基本微粒群算法难以处理高维度目标函数的原因.通过引入信心度和试探策略,算法的收敛速度得到提高;通过引入成功度,搜索过程中的变异概率能够自适应修正.在特定测试函数集上的实验表明,HDOF-PSO在处理高维目标函数时,比基本微粒群算法和一个改进的微粒群算法具有更快的收敛速度和更好的收敛性.

关键词: 群体智能, 微粒群算法, 高维度, 自适应试探, 自适应变异

Abstract: A higher-dimensional-object-function particle swarm optimizer HDOF-PSO algorithm is proposed for the prematurities which are easy to take place when dealing with the higher-dimensional object function by BPSO(basical particle swarm optimizer) algorithm. The reason why HDOF-PSO is difficult to be deal with hasical PSO algorithm is analyzal. The confidence level and trial-and-error strategy are introduced into the algorithm to accelerate its convergence rate with the probability of success also introduced in to enable the adaptive correction available to the probability of mutation in searching process. The experimental results of a specific set of benchmerk functions showed that the HDOF-PSO algorithm has better convergence and higher dimensional object functions.

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