TY - JOUR
T1 - Recent advances in Grey Wolf Optimizer, its versions and applications
T2 - Review
AU - Makhadmeh, Sharif Naser
AU - Al-Betar, Mohammed Azmi
AU - Doush, Iyad Abu
AU - Awadallah, Mohammed A.
AU - Kassaymeh, Sofian
AU - Mirjalili, Seyedali
AU - Zitar, Raed Abu
N1 - Publisher Copyright:
Author
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Classification algorithms
KW - COVID-19
KW - Evolutionary computation
KW - Evolutionary Computation
KW - Grey wolf Optimizer
KW - Mathematical models
KW - Optimization
KW - Particle swarm optimization
KW - Search problems
KW - Swarm Intelligence
UR - http://www.scopus.com/inward/record.url?scp=85168259207&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3304889
DO - 10.1109/ACCESS.2023.3304889
M3 - Article
AN - SCOPUS:85168259207
SN - 2169-3536
SP - 1
JO - IEEE Access
JF - IEEE Access
ER -
