Improved particle swarm optimization algorithm in dynamic environment

被引：0
|
作者：
Xiang, Changcheng [1 ,2 ,3 ]
Tan, Xuegang [3 ]
Yang, Yi [3 ]
机构：
[1] Hubei Minzu Univ, Key Lab Biol Resources Protect & Utilizat, Enshi 445000, Hubei, Peoples R China
[2] Hubei Minzu Univ, Coll Forestry & Horticulture, Enshi 445000, Peoples R China
[3] Hubei Minzu Univ, Coll Sci, Enshi 445000, Peoples R China
来源：
26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC) | 2014年
关键词：
Particle Swarm Optimization; Dynamic Environment; Convergence;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, The improved Particle Swarm Optimization in dynamic objective function environment(DOFPSO) is purposed. The dynamic environment will change with the time t. The DOFPSO algorithm discuss that how to determine changes of the time(environment) and how to keep population diversity. The improved algorithm has the ability to fast response the change of environment and could find the best fitness value quickly. The results of experiment indicate that DOFPSO is more effective than particle swarm optimization(PSO) and restart method particle swarm optimization(RMPSO) in the response of change of environment and fast convergence.
引用
收藏
页码：3098 / 3102
页数：5
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