Assessment of Grey Wolf Optimizer and Its Variants on Benchmark Functions

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dc.contributor.authorVarol Altay E.
dc.contributor.authorAltay O.
dc.date.accessioned2024-07-22T08:03:28Z
dc.date.available2024-07-22T08:03:28Z
dc.date.issued2023
dc.description.abstractOne of the most current metaheuristic swarm intelligence algorithms is the Grey Wolf Optimizer (GWO). Since the number of parameters is small and there is no need for information during the first search, GWO has been adapted to different optimization problems, giving it superiority over other metaheuristic methods. At the same time, it is easy to use, simple, scalable, and adaptable, with its unique ability to provide the ideal balance throughout the search, leading to positive convergence. As a result, the GWO has lately attracted a large research audience from a variety of areas in a very short amount of time. However, it has some disadvantages, such as a slow convergence rate, low sensitivity, and so on, which are seen in the vast majority of metaheuristic methods. Various versions of the current GWO have been proposed to eliminate them. In this article, GWO, improved GWO (IGWO), and augmented GWO (AGWO) methods are examined, and the performances of these methods are discussed in CEC’20 functions and analyzed statistically. The results of the studies demonstrated that IGWO outperformed standard GWO and AGWO. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.DOI-ID10.1007/978-3-031-27099-4_5
dc.identifier.issn23673370
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12275
dc.language.isoEnglish
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.subjectGlobal optimization
dc.subject'current
dc.subjectBenchmark functions
dc.subjectCEC’20 function
dc.subjectGlobal optimisation
dc.subjectGray wolf optimizer
dc.subjectGray wolves
dc.subjectMeta-heuristic methods
dc.subjectMetaheuristic
dc.subjectOptimizers
dc.subjectSwarm intelligence algorithms
dc.subjectSwarm intelligence
dc.titleAssessment of Grey Wolf Optimizer and Its Variants on Benchmark Functions
dc.typeConference paper

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