.png){kind=icon}
返回
A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2015.04.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Particle swarm optimization (PSO)
Multiple subpopulations
Multimodal optimization problem
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
暂无机构信息
引用论文
.png){kind=icon}
A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design基于竞争与合作协同进化的多目标粒子群算法设计
PREAI
分享收藏Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems线性和非线性互联电力系统自动发电控制的混合BFOA-PSO算法
PREAI
分享收藏
查看更多内容