计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

动态环境下运用对称位移映射的PSO 算法

刘子坤,李枚毅,张 晓   

  1. (湘潭大学信息工程学院,湖南湘潭411105)
  • 收稿日期:2013-11-11 出版日期:2014-11-15 发布日期:2014-11-13
  • 作者简介:刘子坤(1989 - ),男,硕士研究生,主研方向:智能计算;李枚毅,教授、博士;张 晓,硕士研究生。
  • 基金项目:
    国家自然科学基金资助项目(61070232)。

PSO Algorithm with Symmetric Displacement Mapping in Dynamic Environment

LIU Zikun,LI Meiyi,ZHANG Xiao   

  1. (School of Information Engineering,Xiangtan University,Xiangtan 411105,China)
  • Received:2013-11-11 Online:2014-11-15 Published:2014-11-13

摘要: 粒子群优化算法在求解动态优化问题时存在多样性缺失和寻优效率低的问题,为此,提出一种运用对称位移映射的双子群算法。该算法通过2 组相互协同的主、辅子群并行地搜索变化的最优值。辅子群采取差异进化机制不断探索新环境,在感知环境变化时引入一种对称位移映射策略,使粒子对称分布在最优解的周围,以提高算法收敛到最优解的概率。使用MPB 和DF1 两种经典的Benchmark 测试函数生成复杂的动态环境,对该算法进行实验仿真,结果表明,该算法能提高跟踪动态变化极值的准确性。

关键词: 粒子群优化, 动态环境, 优化问题, 双子群协同, 对称位移映射, 差异进化

Abstract: Particle Swarm Optimization (PSO) algorithm is inclined to fall into diversity loss and low optimizing efficiency in dynamic environment. In this paper,a PSO algorithm with symmetric displacement mapping is proposed. The main subpopulation and assistant subpopulation particle swarm work with each other to search the changing global optimum by the parallel searching. The assistant subpopulation particle swarm uses differential evolutionary mechanism to constantly explore the new environment,when the environment is changed,and a symmetrical displacement mapping strategy is introduced to improve the convergence probability to the global optimum through symmetrical particle distribution surrounding the global optimum. The simulative environment in experiments is generated by MPB and DF1 two benchmark functions,the results demonstrate that the algorithm can improve the accuracy of tracking the changing global optimum.

Key words: Particle Swarm Optimization ( PSO ), dynamic environment, optimization problem, two subpopulation swarm cooperation, symmetric displacement mapping, differential evolution

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