《计算机应用研究》|Application Research of Computers

基于等高替换和随机反向的粒子群算法

Particle swarm optimization algorithm based on equal replacement and random opposition

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作者 陈群林,高岳林,郭祥
机构 北方民族大学 信息与系统科学研究所,银川 750021
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2017)08-2364-04
DOI 10.3969/j.issn.1001-3695.2017.08.029
摘要 针对标准粒子群优化算法不易跳出局部寻优、搜索精度低等缺陷,提出了等高随机替换策略,运用简化粒子群算法进行更新,加快了粒子寻优能力;并且对适应值最差的一部分粒子,采用了最优随机反方向搜索策略,保证了算法的全局搜索能力。对七个不同类型的测试函数进行仿真实验,结果表明了改进的算法能很好地保持粒子多样性,全局搜索能力强,拥有更好的收敛速度和寻优精度。
关键词 简化粒子群;等高替换;最优随机反向
基金项目 国家自然科学基金资助项目(6156001)
北方民族大学重点科研资助项目(2015KJ10)
陕西省自然科学基础研究计划资助项目(2014JM2-6098)
本文URL http://www.arocmag.com/article/01-2017-08-029.html
英文标题 Particle swarm optimization algorithm based on equal replacement and random opposition
作者英文名 Chen Qunlin, Gao Yuelin, Guo Xiang
机构英文名 ResearchInstituteofInformation&SystemScience,BeifangUniversityofNationalities,Yinchuan750021,China
英文摘要 In order to overcome the shortcomings of conventional particle swarm optimization (PSO) algorithm, such as easily trapping in local optima and lower search accuracy, this paper proposed a different random replacement strategy based on contour. On the basis of the strategy, the algorithm used the simplified particle swarm to update particles, and it speeded up the searching capability of the particled. In order to ensure the global search ability of the algorithm, it put forward the optimal random search strategy in the opposite direction for some particles with worst adaptive value. The algorithm tested on seven distinct types of benchmark functions. The results show that the proposed algorithm can maintain the diversity of particles with strong global search capability, with higher convergence rate and accuracy.
英文关键词 simple particle swarm; equal replacement; random opposition of optimal
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收稿日期 2016/5/14
修回日期 2016/7/15
页码 2364-2367
中图分类号 TP301.6
文献标志码 A