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Computer Engineering ›› 2010, Vol. 36 ›› Issue (19): 24-26. doi: 10.3969/j.issn.1000-3428.2010.19.008

• Networks and Communications • Previous Articles     Next Articles

Population Diffuse PSO Algorithm in Dynamic Environment

ZHAO Chuan-xin1,2, WANG Ru-chuan1,3, JI Yi-mu3   

  1. (1. School of Computer Science and Technology, Soochow University, Suzhou 215006, China; 2. School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241003, China; 3. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
  • Online:2010-10-05 Published:2010-09-27

动态环境下的种群扩散粒子群优化算法

赵传信1,2,王汝传1,3,季一木3   

  1. (1. 苏州大学计算机科学与技术学院,江苏 苏州 215006;2. 安徽师范大学数学计算机科学学院,安徽 芜湖 241003; 3. 南京邮电大学计算机学院,南京 210003)
  • 作者简介:赵传信(1977-),男,博士研究生,主研方向:智能优化,无线自组织网络;王汝传,教授、博士生导师;季一木,讲师、 博士
  • 基金资助:
    国家自然科学基金资助项目(60773041);江苏省自然科学基金资助项目(BK2008451);安徽师范大学校青年基金资助项目(2008xqn48)

Abstract: It is difficult for PSO to detect dynamic change of environment and response in optimizing process. Aiming at the problems, by adding particles which are on the periphery for detecting the change of environment, this paper proposes a new diffuse population function to respond change, and designs an algorithm named Diffuse Particle Swarm Optimization(DPSO). Comparison with APSO and CPSO, it can detect changes of environment more effectively and track with optimum solution faster.

Key words: PSO algorithm, diversity, dynamic environment, diffuse

摘要: 传统的粒子群优化算法在优化过程中难以有效地监测环境的动态变化和响应。针对上述问题,通过增加外围监测粒子加强监测有效性,提出一种可以动态响应环境变化的种群多样性扩散函数,在此基础上设计一种扩散粒子群优化算法(DPSO),在动态环境中与APSO、CPSO进行比较,实验结果表明,DPSO可以更有效地跟踪动态环境下极值的变化并快速收敛。

关键词: 粒子群优化算法, 多样性, 动态环境, 扩散

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