计算机工程与应用Issue(8)：40-44,5.DOI:10.3778/j.issn.1002-8331.1207-0262
自适应惯性权重的分组并行粒子群优化算法
Grouping parallel particle swarm optimization algorithm with adaptive inertia weight
摘要
Abstract
No fundamental change in the particle velocity update for the island model parallel particle swarm optimiza-tion, a grouping parallel particle swarm optimization with adaptive inertia weight is proposed in this paper. This algorithm can adaptively choose the number of particles joining the group in an iterative process, besides, it can adjust the inertia weight of each group adaptively in accordance with the change of the optimal position. Each grouping uses multithreading technology to parallel processing and new information sharing mode is used between particles. The simulation results show that the algorithm has higher convergence speed and convergence precision.关键词
分组/并行/粒子群/自适应/惯性权重Key words
grouping/parallel/particle swarm/adaptive/inertia weight分类
信息技术与安全科学引用本文复制引用
周飞红,廖子贞..自适应惯性权重的分组并行粒子群优化算法[J].计算机工程与应用,2014,(8):40-44,5.基金项目
湖南省教育厅科学研究项目（No.10C0911）。 （）
