A Review on Hybridization of Particle Swarm Optimization with Artificial Bee Colony

被引：3

作者：

Xin, Bin ^{[1
,2
,3
]}

Wang, Yipeng ^{[1
]}

Chen, Lu ^{[1
]}

Cai, Tao ^{[1
,2
]}

Chen, Wenjie ^{[1
,2
]}

机构：

[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

[2] Beijing Inst Technol, Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China

[3] Beijing Adv Innovat Ctr Intelligent Robots & Syst, Beijing 100081, Peoples R China

来源：

ADVANCES IN SWARM INTELLIGENCE, ICSI 2017, PT II | 2017年 / 10386卷

基金：

中国国家自然科学基金; 高等学校博士学科点专项科研基金;

关键词：

Particle swarm optimization (PSO); Artificial bee colony (ABC); Hybridization; Review; Swarm intelligence; METAHEURISTICS; ALGORITHM; TAXONOMY;

D O I：

10.1007/978-3-319-61833-3_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Particle swarm optimization (PSO) and artificial bee colony (ABC) are two formidable population-based optimizers inspired by swarm intelligence(SI). They follow different philosophies and paradigms, and both are successfully and widely applied in scientific and engineering research. The hybridization of PSO and ABC represents a promising way to create more powerful SI-based hybrid optimizers, especially for specific problem solving. In the past decade, numerous hybrids of ABC and PSO have emerged with diverse design ideas from many researchers. This paper is aimed at reviewing the existing hybrids based on PSO and ABC and giving a classification and an analysis of them.

引用

页码：242 / 249

页数：8

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