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Review of the grey wolf optimization algorithm: variants and applications

One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and...

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Main Authors:	Liu, Yunyun, As’arry, Azizan, Hassan, Mohd Khair, Hairuddin, Abdul Aziz, Mohamad, Hesham
Format:	Article
Published:	Springer 2024
Online Access:	http://psasir.upm.edu.my/id/eprint/105664/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177585429&doi=10.1007%2fs00521-023-09202-8&partnerID=40&md5=009f987c191db953b9d222de80a4ada4
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spelling	my.upm.eprints.1056642024-02-08T02:44:35Z http://psasir.upm.edu.my/id/eprint/105664/ Review of the grey wolf optimization algorithm: variants and applications Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz Mohamad, Hesham One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. Springer 2024 Article PeerReviewed Liu, Yunyun and As’arry, Azizan and Hassan, Mohd Khair and Hairuddin, Abdul Aziz and Mohamad, Hesham (2024) Review of the grey wolf optimization algorithm: variants and applications. Neural Computing and Applications, 36 (6). pp. 2713-2735. ISSN 0941-0643; ESSN: 1433-3058 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177585429&doi=10.1007%2fs00521-023-09202-8&partnerID=40&md5=009f987c191db953b9d222de80a4ada4 10.1007/s00521-023-09202-8
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
description	One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
format	Article
author	Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz Mohamad, Hesham
spellingShingle	Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz Mohamad, Hesham Review of the grey wolf optimization algorithm: variants and applications
author_facet	Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz Mohamad, Hesham
author_sort	Liu, Yunyun
title	Review of the grey wolf optimization algorithm: variants and applications
title_short	Review of the grey wolf optimization algorithm: variants and applications
title_full	Review of the grey wolf optimization algorithm: variants and applications
title_fullStr	Review of the grey wolf optimization algorithm: variants and applications
title_full_unstemmed	Review of the grey wolf optimization algorithm: variants and applications
title_sort	review of the grey wolf optimization algorithm: variants and applications
publisher	Springer
publishDate	2024
url	http://psasir.upm.edu.my/id/eprint/105664/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177585429&doi=10.1007%2fs00521-023-09202-8&partnerID=40&md5=009f987c191db953b9d222de80a4ada4
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score	13.211869

Review of the grey wolf optimization algorithm: variants and applications

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