Prioritized Multi-Step Decision-Making Gray Wolf Optimization Algorithm for Engineering Applications

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dc.authorid0000-0002-4353-1261
dc.authorid0000-0002-4049-0716
dc.contributor.authorDagal, Idriss
dc.contributor.authorDemirci, Alpaslan
dc.contributor.authorHarrison, Ambe
dc.contributor.authorMbasso, Wulfran Fendzi
dc.contributor.authorTercan, Said Mirza
dc.contributor.authorAkin, Burak
dc.contributor.authorTanrioven, Kuersat
dc.date.accessioned2026-01-31T15:08:08Z
dc.date.available2026-01-31T15:08:08Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThis article introduces the Prey-Movement Strategy Gray Wolf Optimizer (PMS-GWO), an enhanced version of the Gray Wolf Optimizer (GWO) designed to improve optimization efficiency through a novel multi-step decision-making process. By integrating adaptive exploration-exploitation strategies, PMS-GWO dynamically manages leadership roles, balances local and global searches, and introduces a prey escape mechanism, significantly improving solution diversity. Comparative analysis across 23 benchmark functions demonstrates PMS-GWO's superior performance, achieving up to 28.6% faster convergence and a 55.5%-93.8% increase in solution accuracy compared to the standard GWO. Notably, PMS-GWO enhances computational efficiency by 21.7%-27.4% and shows a 168.8% improvement in solution accuracy for the complex Michalewicz function over the baseline GWO. Visual convergence speed analysis, evidenced by a rapid fitness value decline within 100 iterations, reveals PMS-GWO's quickest convergence time of 0.02 s among tested algorithms. Furthermore, a comparison of runtime for several algorithms, including PMS-GWO, MMCCS-GWO, CC-GWO, MGWO, and GWO, clearly indicates that PMS-GWO achieves the lowest runtime of 2.364 s, significantly faster than CC-GWO and MGWO, which both exceed 5 s. This visual representation highlights the computational efficiency of PMS-GWO compared to other algorithms. PMS-GWO also outperforms advanced GWO variants like MMSCC-GWO, MGWO, and CCS-GWO, particularly in complex optimization landscapes, highlighting its adaptability and effectiveness for real-world applications in energy systems and engineering design. The multi-step decision-making process implemented in PMS-GWO is critical to achieving these improved convergence and diversity metrics.
dc.identifier.doi10.1002/eng2.70154
dc.identifier.issn2577-8196
dc.identifier.issue5
dc.identifier.scopus2-s2.0-105004639985
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org./10.1002/eng2.70154
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10594
dc.identifier.volume7
dc.identifier.wosWOS:001496125900032
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofEngineering Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectdynamic role reassignment
dc.subjectexploration-exploitation balance
dc.subjectgray wolf optimizer (GWO)
dc.subjectmulti-objective optimization
dc.subjectprey mimicking and escape mechanism
dc.subjectprey-movement strategy
dc.titlePrioritized Multi-Step Decision-Making Gray Wolf Optimization Algorithm for Engineering Applications
dc.typeArticle

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