Multi-objective particle swarm optimization based on adaptive grid algorithms

被引：0

作者：

Yang, Junjie ^{[1
]}

Zhou, Jianzhong ^{[1
]}

Liu, Fang ^{[1
]}

Fang, Rengcun ^{[1
]}

Zhong, Jianwei ^{[2
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Peoples R China

[2] Hubei Inst Natl, Coll Informat Engn, Enshi 445000, Peoples R China

来源：

DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS | 2007年 / 14卷

关键词：

evolutionary algorithms; particle swarm optimization; adaptive grid algorithms; multiple objectives;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Multi-objective particle swarm optimization based on adaptive grid algorithms (AG-MOPSO) is presented in this paper by investigating on the density information estimation algorithm, Pareto optimal solution searching mechanism and Archive pruning techniques of multi-objective evolutionary algorithms (MOEAs). The proposed algorithms can obtain the valid density value of particles by adopting the adaptive grid algorithms, guide the particles searching efficiently in problem space and delete inferior particles by respectively employing Pareto optimal solution searching algorithm and Archive pruning techniques based on adaptive grid algorithms. Six well-designed test problems are used to evaluate the developed AG-MOPSO. Compared with the representative MOEAs, AG-MOPSO shows its effectiveness and efficiency in solving complex large scale optimization problems.

引用

页码：687 / 694

页数：8

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