张捷, 封俊红. 基于动态距离阈值的并行微粒群优化[J]. 微电子学与计算机, 2010, 27(7): 78-81,85.
引用本文: 张捷, 封俊红. 基于动态距离阈值的并行微粒群优化[J]. 微电子学与计算机, 2010, 27(7): 78-81,85.
ZHANG Jie, FENG Jun-hong. The Parellel Particle Swarm Optimization Based on Dynamic Distance Threshold[J]. Microelectronics & Computer, 2010, 27(7): 78-81,85.
Citation: ZHANG Jie, FENG Jun-hong. The Parellel Particle Swarm Optimization Based on Dynamic Distance Threshold[J]. Microelectronics & Computer, 2010, 27(7): 78-81,85.

基于动态距离阈值的并行微粒群优化

The Parellel Particle Swarm Optimization Based on Dynamic Distance Threshold

  • 摘要: 通过给标准微粒群引入动态距离阈值,将微粒分为最佳位置附近、平均位置附近和其他3类,形成3个子种群.让最佳位置附近的微粒进行集中式细搜索,让平均位置附近的微粒进行一般搜索,让其他微粒进行分散式粗搜索,合理地平衡了粗搜索和细搜索的矛盾,使得在微粒多样性保持基本稳定的情况下,实现了收敛速度的提高.采用并行算法来加快计算速度,也可以保持群体的多样性,易于跳出局部最优.通过仿真实验证实了这种算法是既能增加收敛性又能提高微粒的多样性.

     

    Abstract: Through introducing dynamic distance threshold to standard PSO, the particles are divided into three categories of the vicinity of the best position, the vicinity of the average position and the other position, which are formed three Subspecies. The first category execute concentrated search, the second category do general search and the third category do scatter search, which reasonably balance the contradiction of coarse search and detailed search. This bring about that convergence speed is improved under the circumstance of keeping fundamental stable condition in particle diversity. The parallel algorithm is used to speed up calculating speed, to keep the particle diversity and to jump out local optimization. The simulating experiments have certified that the algorithm can improve not only the convergence but also the particle diversity.

     

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