计算机工程 ›› 2012, Vol. 38 ›› Issue (16): 167-169.doi: 10.3969/j.issn.1000-3428.2012.16.043

• 人工智能及识别技术 • 上一篇    下一篇

保持多样性的自适应动态粒子群算法及其应用

逄金梅 1,2,郑向伟 1,2,王智昊 1,2   

  1. (1. 山东师范大学信息科学与工程学院,济南 250014;2. 山东省分布式计算机软件新技术重点实验室,济南 250014)
  • 收稿日期:2011-10-17 修回日期:2012-12-04 出版日期:2012-08-20 发布日期:2012-08-17
  • 作者简介:逄金梅(1987-),女,硕士研究生,主研方向:群体智能;郑向伟,副教授、博士;王智昊,硕士研究生
  • 基金项目:

    山东省高等学校科技计划基金资助项目(J10LG08);山东省优秀中青年科学家科研奖励基金资助项目(BS2010DX033)

Adaptive Dynamic Particle Swarm Algorithm with Diversity Preservation and Its Application

PANG Jin-mei 1,2, ZHENG Xiang-wei 1,2, WANG Zhi-hao 1,2   

  1. (1. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China; 2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan 250014, China)
  • Received:2011-10-17 Revised:2012-12-04 Online:2012-08-20 Published:2012-08-17

摘要: 针对动态环境中的种群多样性问题,提出一种保持种群多样性的双子群粒子群优化算法。将群搜索算法中的游走者思想引入到粒子群优化算法中,基于群体多样性,子种群B采用不同的方法更新速度和位置,子种群A和子种群B交换最优信息,扩展种群的搜索范围,增强整个群体的多样性水平。将改进的算法应用于复杂变化的抛物线函数和群体动画的跟随效果中,结果表明该算法在动态环境中的有效性,并能够真实模拟群体跟随行为。

关键词: 动态粒子群优化, 多样性, 双种群, 群搜索, 群体动画

Abstract: A double population particle swarm optimization with adaptive diversity preservation is proposed considering the population diversity in dynamic environment. The ranger idea of group search is introduced to particle swarm optimization, where subswarm B updates its speeds and positions with different methods according to the diversity of particle swarm and subswarm A and B exchange their optima. These mechanisms extend the search range and improve the swarm diversity. The scheme is tested on benchmark functions with dynamic complex changes and the simulation results show the proposed algorithm is effective in dynamic environments. It is also used to simulate group following behavior.

Key words: dynamic particle swarm optimization, diversity, double population, group search, group animation

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