计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (35): 34-37.DOI: 10.3778/j.issn.1002-8331.2009.35.011

• 研究、探讨 • 上一篇    下一篇

二分微粒群协同进化优化算法

姚祥光1,周永权2,李咏梅1   

  1. 1.广西大学 计算机与电子信息学院,南宁 530004
    2.广西民族大学 数学与计算机科学学院,南宁 530006
  • 收稿日期:2009-09-11 修回日期:2009-10-16 出版日期:2009-12-11 发布日期:2009-12-11
  • 通讯作者: 姚祥光

Two sub-swarms co-evolution Particle Swarm Optimization algorithm

YAO Xiang-guang1,ZHOU Yong-quan2,LI Yong-mei1   

  1. 1.College of Computer and Electron Information,Guangxi University,Nanning 530004,China
    2.College of Mathematics and Computer Science,Guangxi University for Nationalities,Nanning 530006,China
  • Received:2009-09-11 Revised:2009-10-16 Online:2009-12-11 Published:2009-12-11
  • Contact: YAO Xiang-guang

摘要: 针对微粒群优化算法易发生过早收敛问题,受自然界分而治之的思想和共生现象的启发,提出了一种二分微粒群协同进化优化算法,算法的主要思想是在奇数次对种群进行寻优,在偶数次将微粒群分为两个子种群,子种群独立完成寻优任务,与其他群体几乎不发生联系。最后,通过对5个标准函数的测试结果表明,提出的算法在一定程度上避免了陷入局部极值点,并且提高了收敛精度。

Abstract: The particle swarm optimization algorithm is a new branch in evolution computing field.This algorithm is simple and effective,and is easy in premature convergence.According to the idea of divide and conquer and the enlightenment of co-evolution,two sub-swarms co-evolution particle swarm optimization algorithm is presented.When it is odd number,the algorithm searches and finds optimization in all particles,else particles are divided into two sub-swarms;they implement their own tasks without almost any connection with others.By doing experiments on four benchmark functions,the results show that the algorithm avoids trapping into local optimum in a certain extent and improves the precision of convergence.

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