计算机应用 ›› 2011, Vol. 31 ›› Issue (05): 1324-1327.DOI: 10.3724/SP.J.1087.2011.01324

• 人工智能 • 上一篇    下一篇

改进公式的核心主子群粒子群算法

随聪慧,唐慧佳   

  1. 西南交通大学 信息科学与技术学院,成都 610031
  • 收稿日期:2010-09-20 修回日期:2010-11-22 发布日期:2011-05-01 出版日期:2011-05-01
  • 通讯作者: 随聪慧
  • 作者简介:随聪慧(1982-),女,内蒙古赤峰人,硕士研究生,主要研究方向:智能计算、电子商务;唐慧佳(1964-),女,四川绵阳人,副教授,主要研究方向:电子商务、计算机网络、信息处理、网络化制造。
  • 基金资助:

    国家科技支撑计划项目(2011BAH211302):四川省科技攻关课题(2009GE0016)。

Core master group PSO based on improvement formula

SUI Cong-hui, TANG Hui-jia   

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China
  • Received:2010-09-20 Revised:2010-11-22 Online:2011-05-01 Published:2011-05-01

摘要: 标准的粒子群算法在其进化的公式中,只是考虑了群体最佳的适应度值和个体最佳适应度值,这导致了标准的粒子群算法在算法的进化后期由于缺乏多样性收敛精度不高。为了提高算法的精度,提出了核心主子群粒子群算法,并将提出的核心主子群算法与改进的公式相结合。通过实验证明,改进算法使得所求结果的精度有进一步的提高。

关键词: 粒子群优化, 多样性, 核心主子群, 适应度值, 收敛

Abstract: The standard particle swarm optimization in the evolution formula only considers both the colony's best fitness value and the individual's fitness values. Therefore, it leads to the low accuracy of the convergence on account of being lack of the diversity in later evolution period. In order to improve the accuracy of the algorithm, the paper proposed the core master group particle swarm, and combined a core master group particle swarm with the improved formula. The improved algorithm is proved through experiments to be more accurate.

Key words: Particle Swarm Optimization (PSO), diversity, core master group, fitness value, convergence