Cloud model particle swarm optimization algorithm based on pattern search method

被引：0
|
作者：
Wu J.-H. [1 ]
Wang B.-H. [1 ]
Zhang X.-G. [2 ]
Chen H. [1 ]
机构：
[1] College of Information Science and Engineering, Hu'nan University, Changsha
[2] College of Electrical and Information Engineering, Hu'nan University, Changsha
来源：
Wu, Jian-Hui (jianhuiw@hnu.edu.cn) | 1600年 / Northeast University卷 / 32期
关键词：
Cloud model; Multimodal function; Particle swarm optimization; Pattern search method;
D O I：
10.13195/j.kzyjc.2016.1116
中图分类号：
学科分类号：
摘要：
In order to overcome the shortcomings of particle swarm optimization(PSO) in solving multimodal function optimization problems which includes easily falling into local minimum, low accuracy and difficultly searching extreme points as many as possible, a novel cloud model particle swarm optimization algorithm based on the pattern search method(PCPSO) is proposed. The cloud model particle swarm optimization(CPSO) algorithm is used to do global searching in the feasible zone, and then the pattern search method(PSM) is used to improve the accuracy of the sub- optimal solution. The simulation tests demonstrate that the proposed method can ensure the convergence speed, meanwhile the convergence accuracy and the number of extreme points are strikingly improved. © 2017, Editorial Office of Control and Decision. All right reserved.
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页码：2076 / 2080
页数：4
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