A hybrid glowworm swarm optimization algorithm to solve constrained multimodal functions optimization

被引：14

作者：

Zhou, Yongquan ^{[1
,2
]}

Zhou, Guo ^{[3
]}

Zhang, Junli ^{[1
]}

机构：

[1] Guangxi Univ Nationalities, Coll Informat Sci & Engn, Nanning, Peoples R China

[2] Guangxi Key Lab Hybrid Computat & IC Design Anal, Nanning, Peoples R China

[3] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China

来源：

OPTIMIZATION | 2015年 / 64卷 / 04期

基金：

美国国家科学基金会;

关键词：

differential evolution; artificial fish swarm; glowworm swarm optimization; multimodal function optimization; hybrid optimization algorithm; 68W20; 68T20; DIFFERENTIAL EVOLUTION;

D O I：

10.1080/02331934.2013.793329

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. The HGSO algorithm embeds predatory behaviour of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the GSO with differential evolution on the basis of a two-population co-evolution mechanism. In addition, to overcome the premature convergence, the local search strategy based on simulated annealing is applied to make the search of GSO approach the true optimum solution gradually. Finally, several benchmark functions show that HGSO has faster convergence efficiency and higher computational precision, and is more effective for solving constrained multi-modal function optimization problems.

引用

页码：1057 / 1080

页数：24

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