Boosted Equilibrium Optimizer Using New Adaptive Search and Update Strategies for Solving Global Optimization Problems

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dc.authoridhttps://orcid.org/0000-0002-4070-4630
dc.authoridhttps://orcid.org/0000-0002-7117-9736
dc.authoridhttps://orcid.org/0000-0001-5069-6470
dc.contributor.authorTuna, Resul
dc.contributor.authorÇelik, Yüksel
dc.contributor.authorFındık, Oğuz
dc.date.accessioned2025-01-06T07:19:22Z
dc.date.available2025-01-06T07:19:22Z
dc.date.issued2024-12-23
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe Equilibrium Optimizer (EO) is an optimization algorithm inspired by a physical law called mass balance, which represents the amount of mass entering, leaving, and being produced in a control volume. Although the EO is a well-accepted and successful algorithm in the literature, it needs improvements in the search, exploration, and exploitation phases. Its main problems include low convergence, getting stuck in local minima, and imbalance between the exploration and exploitation phases. This paper introduces the Boosted Equilibrium Optimizer (BEO) algorithm, where improvements are proposed to solve these problems and improve the performance of the EO algorithm. New methods are proposed for the three important phases of the algorithm: initial population, candidate pool generation, and updating. In the proposed algorithm, the exploration phase is strengthened by using a uniformly distributed random initial population instead of the traditional random initial population and a versatile concentration pool strategy. Furthermore, the balance between the exploration and exploitation phases is improved with two new approaches proposed for the updating phase. These novel methods enhance the algorithm’s performance by more effectively balancing exploration and exploitation. The proposed algorithm is tested using a total of 23 standard test functions, including unimodal, multimodal, and fixed-size multimodal. The results are supported by numerical values and graphs. In addition, the proposed BEO algorithm is applied to solve real-world engineering design problems. The BEO outperforms the original EO algorithm on all problems.
dc.identifier.citationTuna, R., Çelik, Y., & Fındık, O. (2024). Boosted Equilibrium Optimizer Using New Adaptive Search and Update Strategies for Solving Global Optimization Problems. Electronics, 13(24), 5061. https://doi.org/10.3390/electronics13245061
dc.identifier.doi10.3390/electronics13245061
dc.identifier.issn2079-9292
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85213219582
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/electronics13245061
dc.identifier.urihttps://hdl.handle.net/20.500.14619/14947
dc.identifier.volume13
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI AG
dc.relation.ispartofElectronics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectadaptive search
dc.subjectdirectional scanning
dc.subjectequilibrium optimizer
dc.subjectmetaheuristic
dc.subjectoptimization
dc.subjectoptimization algorithm
dc.titleBoosted Equilibrium Optimizer Using New Adaptive Search and Update Strategies for Solving Global Optimization Problems
dc.typeArticle
oaire.citation.issue24
oaire.citation.volume13

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