Monarch butterfly optimization: A comprehensive review

被引：147

作者：

Feng, Yanhong ^{[1
,2
]}

Deb, Suash ^{[3
]}

Wang, Gai-Ge ^{[4
,5
,6
,7
]}

Alavi, Amir H. ^{[8
,9
,10
]}

机构：

[1] Hebei GEO Univ, Sch Informat Engn, Shijiazhuang 050031, Hebei, Peoples R China

[2] Hebei GEO Univ, Hebei Ctr Ecol & Environm Geol Res, Shijiazhuang 050031, Hebei, Peoples R China

[3] Victoria Univ, Decis Sci & Modelling Program, Melbourne, Vic, Australia

[4] Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266100, Peoples R China

[5] Northeast Normal Univ, Inst Algorithm & Big Data Anal, Changchun 130117, Peoples R China

[6] Northeast Normal Univ, Sch Comp Sci & Informat Technol, Changchun 130117, Peoples R China

[7] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China

[8] Univ Pittsburgh, Dept Civil & Environm Engn, Pittsburgh, PA 15261 USA

[9] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan

[10] Univ Pittsburgh, Dept Bioengn, Pittsburgh, PA 15261 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2021年 / 168卷

基金：

中国国家自然科学基金;

关键词：

Evolutionary computations; Monarch butterfly optimization; Swarm intelligence; Metaheuristic; Optimization; PARTICLE SWARM OPTIMIZATION; VEHICLE-ROUTING PROBLEM; BEE COLONY ALGORITHM; DIFFERENTIAL EVOLUTION; SEARCH ALGORITHM; SOLVING SYSTEMS; KRILL HERD; CUCKOO SEARCH; MUTATION; DESIGN;

D O I：

10.1016/j.eswa.2020.114418

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm intelligence metaheuristic algorithm inspired by the migration behavior of monarch butterflies. Through the migration operation and butterfly adjusting operation, individuals in MBO are updated. MBO can outperform many state-of-the-art optimization techniques when solving global numerical optimization and engineering problems. This paper presents a comprehensive review of the MBO algorithm including its modifications, hybridizations, variants, and applications. Additionally, further research directions for MBO are discussed. This review study serves as a solid reference for future studies in the arena of SI and in particular the MBO algorithm.

引用

页数：11

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