A survey of beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing

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dc.authorscopusidBahman Arasteh / 39861139000
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.contributor.authorLee, Sang-Woong
dc.contributor.authorHaider, Amir
dc.contributor.authorRahmani, Amir Masoud
dc.contributor.authorArasteh, Bahman
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorTang, Shengda
dc.contributor.authorLiu, Zhe
dc.contributor.authorAurangzeb, Khursheed
dc.contributor.authorHosseinzadeh, Mehdi
dc.date.accessioned2025-04-17T08:17:19Z
dc.date.available2025-04-17T08:17:19Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractOptimization, as a fundamental pillar in engineering, computer science, economics, and many other fields, plays a decisive role in improving the performance of systems and achieving desired goals. Optimization problems involve many variables, various constraints, and nonlinear objective functions. Among the challenges of complex optimization problems is the extensive search space with local optima that prevents reaching the global optimal solution. Therefore, intelligent and collective methods are needed to solve problems, such as searching for large problem spaces and identifying near-optimal solutions. Metaheuristic algorithms are a successful method for solving complex optimization problems. Usually, metaheuristic algorithms, inspired by natural and social phenomena, try to find optimal or near-optimal solutions by using random searches and intelligent explorations in the problem space. Beluga Whale Optimization (BWO) is one of the metaheuristic algorithms for solving optimization problems that has attracted the attention of researchers in recent years. The BWO algorithm tries to optimize the search space and achieve optimal solutions by simulating the collective behavior of whales. A study and review of published articles on the BWO algorithm show that this algorithm has been used in various fields, including optimization of mathematical functions, engineering problems, and even problems related to artificial intelligence. In this article, the BWO algorithm is classified according to four categories (combination, improvement, variants, and optimization). An analysis of 151 papers shows that the BWO algorithm has the highest percentage (49%) in the improvement field. The combination, variants, and optimization fields comprise 12%, 7%, and 32%, respectively.
dc.description.sponsorshipKing Saud University
dc.identifier.citationLee, S. W., Haider, A., Rahmani, A. M., Arasteh, B., Gharehchopogh, F. S., Tang, S., ... & Hosseinzadeh, M. (2025). A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing. Computer Science Review, 57, 100740.
dc.identifier.doi10.1016/j.cosrev.2025.100740
dc.identifier.endpage30
dc.identifier.issn1574-0137
dc.identifier.issn1876-7745
dc.identifier.scopus2-s2.0-85219147873
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.cosrev.2025.100740
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6155
dc.identifier.volume57
dc.identifier.wosWOS:001439704800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArasteh, Bahman
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.language.isoen
dc.publisherElsevier Ireland ltd
dc.relation.ispartofComputer science review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBeluga Whale Optimization Algorithm
dc.subjectCollective Behaviors
dc.subjectMetaheuristic Algorithms
dc.subjectOptimization
dc.titleA survey of beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing
dc.typeArticle

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