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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| Format: | Article |
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Springer Science and Business Media Deutschland GmbH
2023
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| _version_ | 1811138007755915264 |
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| author | Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz |
| author_facet | Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz |
| author_sort | Liu, Yunyun |
| collection | UPM |
| 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. |
| first_indexed | 2024-09-25T03:43:21Z |
| format | Article |
| id | upm.eprints-110565 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:43:21Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | upm.eprints-1105652024-05-15T16:38:43Z http://psasir.upm.edu.my/id/eprint/110565/ Review of the grey wolf optimization algorithm: variants and applications Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz 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. Springer Science and Business Media Deutschland GmbH 2023 Article PeerReviewed Liu, Yunyun and As’arry, Azizan and Hassan, Mohd Khair and Hairuddin, Abdul Aziz (2023) Review of the grey wolf optimization algorithm: variants and applications. Neural Computing & Applications, 36 (6). pp. 2713-2735. ISSN 0941-0643; ESSN: 1433-3058 https://link.springer.com/article/10.1007/s00521-023-09202-8 10.1007/s00521-023-09202-8 |
| spellingShingle | Liu, Yunyun As’arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz Review of the grey wolf optimization algorithm: variants and applications |
| title | 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_short | Review of the grey wolf optimization algorithm: variants and applications |
| title_sort | review of the grey wolf optimization algorithm variants and applications |
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