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

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dc.authoridFINDIK, OGUZ/0000-0001-5069-6470
dc.authoridCELIK, YUKSEL/0000-0002-7117-9736
dc.contributor.authorTuna, Resul
dc.contributor.authorCelik, Yuksel
dc.contributor.authorFindik, Oguz
dc.date.accessioned2025-03-23T19:26:28Z
dc.date.available2025-03-23T19:26:28Z
dc.date.issued2024
dc.departmentSinop Üniversitesi
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.doi10.3390/electronics13245061
dc.identifier.issn2079-9292
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85213219582
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://doi.org/10.3390/electronics13245061
dc.identifier.urihttps://hdl.handle.net/11486/4695
dc.identifier.volume13
dc.identifier.wosWOS:001387701900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofElectronics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectequilibrium optimizer
dc.subjectoptimization
dc.subjectadaptive search
dc.subjectmetaheuristic
dc.subjectoptimization algorithm
dc.subjectdirectional scanning
dc.titleBoosted Equilibrium Optimizer Using New Adaptive Search and Update Strategies for Solving Global Optimization Problems
dc.typeArticle

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