Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adapt...

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Published inIEEE access Vol. 12; pp. 22991 - 23028
Main Authors Makhadmeh, Sharif Naser, Al-Betar, Mohammed Azmi, Doush, Iyad Abu, Awadallah, Mohammed A., Kassaymeh, Sofian, Mirjalili, Seyedali, Zitar, Raed Abu
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial intelligence
Bioinformatics
Biomedical engineering
Classification algorithms
Computer Science
COVID-19
Evolutionary computation
Grey Wolf Optimizer
Mathematical models
Optimization
Particle swarm optimization
Search problems
Swarm intelligence
Evolutionary Computation
Grey wolf Optimizer
Optimization
Swarm Intelligence
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Abstract The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.
AbstractList The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO’s appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.
Author Awadallah, Mohammed A.
Al-Betar, Mohammed Azmi
Zitar, Raed Abu
Kassaymeh, Sofian
Makhadmeh, Sharif Naser
Doush, Iyad Abu
Mirjalili, Seyedali
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  orcidid: 0000-0003-2693-2132
  surname: Zitar
  fullname: Zitar, Raed Abu
  organization: Sorbonne Center of Artificial Intelligence, Sorbonne University Abu Dhabi, Abu Dhabi, United Arab Emirates
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Mon Jun 30 04:30:37 EDT 2025
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Tue Jul 01 02:49:15 EDT 2025
Wed Aug 27 02:17:05 EDT 2025
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Keywords Evolutionary Computation
Grey wolf Optimizer
Optimization
Swarm Intelligence
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Snippet The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf...
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SubjectTerms Artificial intelligence
Bioinformatics
Biomedical engineering
Classification algorithms
Computer Science
COVID-19
Evolutionary computation
Grey Wolf Optimizer
Mathematical models
Optimization
Particle swarm optimization
Search problems
Swarm intelligence
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Title Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review
URI https://ieeexplore.ieee.org/document/10215385
https://www.proquest.com/docview/2927660733
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Volume 12
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