计算机应用 ›› 2013, Vol. 33 ›› Issue (02): 319-322.DOI: 10.3724/SP.J.1087.2013.00319

• 先进计算 • 上一篇    下一篇

基于余弦函数改进的PSO算法及其仿真

张敏1,黄强1,许周钊2,姜柏庄1   

  1. 1. 湖南科技大学 信息与电气工程学院,湖南 湘潭 411201
    2. 湖南省电力公司 岳阳汨罗电力局,湖南 岳阳 414400
  • 收稿日期:2012-08-27 修回日期:2012-10-31 出版日期:2013-02-01 发布日期:2013-02-25
  • 通讯作者: 张敏
  • 作者简介:张敏(1963-),男,湖南邵阳人,教授,博士,主要研究方向:非线性控制系统分析与控制;
    黄强(1985-),男,湖南邵阳人,硕士研究生,主要研究方向:复杂系统分析与控制、系统智能优化;
    许周钊(1988-),男,湖南岳阳人,主要研究方向:电力系统继电保护、系统优化;
    姜柏庄(1987-),男,吉林长春人,硕士研究生,主要研究方向:电机调速、人工智能计算。
  • 基金资助:
    湖南省教育厅资助项目;湖南科技大学创新基金资助项目

Improved PSO algorithm based on cosine functions and its simulation

ZHANG Min1,HUANG Qiang1,XU Zhouzhao2,JIANG Baizhuang1   

  1. 1. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan Hunan 411201, China
    2. Electric Power Bureau of Miluo in Yueyang, Electric Power Corporation of Hunan Province, Yueyang Hunan 414400, China
  • Received:2012-08-27 Revised:2012-10-31 Online:2013-02-01 Published:2013-02-25
  • Contact: ZHANG Min
  • Supported by:
    A Project Supported by Scientific Reaearch Fund of Hunan Provincial Education Department

摘要: 粒子群算法具有简单、易于实现等优点在科学与工程领域得到了很好的验证,但是粒子群优化算法与其他进化算法一样存在容易陷入局部极小和早熟收敛等缺点。分析了其存在缺点的主要原因,并此基础上提出了一种改进的粒子群算法(CPSO)。利用余弦函数非线性改变惯性权重、对称改变学习因子进一步提高了粒子的学习能力,同时引入了细菌趋化操作用以维持种群多样性,使得CPSO算法性能在一定程度上优于标准粒子群(SPSO)算法。利用五个标准测试函数对三种算法的仿真结果进行可对比分析,分析结果表明:CPSO算法能在一定程度上跳出局部最优,有效地避免了SPSO算法早熟收敛问题,并具有较快的收敛速度。

关键词: 粒子群优化, 惯性权重, 学习因子, 细菌趋化, 种群多样性, 早熟收敛

Abstract: The advantages of simplicity and easy implementation of Particle Swarm Optimization (PSO) algorithm have been validated in science and engineering fields. However, the weaknesses of PSO algorithm are the same as that of other evolutionary algorithms, such as being easy to fall into local minimum, premature convergence. The causes of these disadvantages were analyzed, and an improved algorithm named Cosine PSO (CPSO) was proposed, in which the inertia weight of the particle was nonlinearly adjusted based on cosine functions and the learning factor was symmetrically changed, as well as population diversity was maintained based on bacterial chemotaxis. Therefore, CPSO algorithm is better than the Standard PSO (SPSO) in a certain degree. Simulation comparison of the three algorithms on five standard test functions indicates that, CPSO algorithm not only jumps out of local optimum and effectively alleviates the problem of premature convergence, but also has fast convergence speed.

Key words: Particle Swarm Optimization (PSO), inertia weight, learning factor, bacterial chemotaxis, population diversity, premature convergence

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