Hybridization strategies in swarm intelligence: the case of ABC–FA and ABC–RUN algorithms

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dc.authorid0000-0002-3645-3445
dc.contributor.authorYelmenoğlu, Elif Denizen_US
dc.contributor.editorPajenado, Rex S.en_US
dc.contributor.editorDilli, Şirinen_US
dc.date.accessioned2026-02-19T10:10:16Z
dc.date.available2026-02-19T10:10:16Z
dc.date.issued2025-10-08
dc.departmentIşık Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Enformasyon Teknolojileri Bölümüen_US
dc.departmentIşık University, Faculty of Economics, Administrative and Social Sciences, Department of Information Technologiesen_US
dc.description.abstractMetaheuristic optimization algorithms have become a very popular field of study in recent years due to their ability to effectively solve complex, multidimensional problems. In this study, the Artificial Bee Colony (ABC), Firefly Algorithm (FA), and Runge–Kutta (RUN) optimization algorithms, known for their good performance among metaheuristic methods, are compared with their hybrid variants ABC_RUN and ABC_FA. Five widely used benchmark functions were selected for performance evaluation, and the performance results of the algorithms were statistically evaluated using the Wilcoxon signed-rank test. Furthermore, convergence curves were generated to show the average performance of the algorithms, and average running times were calculated to examine the balance between accuracy and computational cost. The findings show that hybrid methods provide higher accuracy compared to classical methods, while the RUN algorithm has an advantage in terms of running time. This comparative analysis demonstrates that hybrid approaches can more effectively balance exploration and exploitation, increase global optimization performance, and are applicable to real-world problems.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationYelmenoğlu, E. D. (2025). Hybridization strategies in swarm intelligence: the case of ABC–FA and ABC–RUN algorithms. Paper presented at the 17th International Istanbul Scientific Research Congress, 1038-1038. doi:https://doi.org/10.30546/19023.978-9952-8596-8-3.2025.0031en_US
dc.identifier.endpage1038
dc.identifier.isbn9789952859683
dc.identifier.startpage1038
dc.identifier.urihttps://hdl.handle.net/11729/7028
dc.identifier.urihttps://doi.org/10.30546/19023.978-9952-8596-8-3.2025.0031
dc.institutionauthorYelmenoğlu, Elif Denizen_US
dc.institutionauthorid0000-0002-3645-3445
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherBZT Turan Publishing Houseen_US
dc.relation.ispartof17th International Istanbul Scientific Research Congressen_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMetaheuristic optimizationen_US
dc.subjectHybrid algorithmsen_US
dc.subjectArtificial bee colonyen_US
dc.subjectFirefly algorithmen_US
dc.subjectRunge kutta optimizationen_US
dc.titleHybridization strategies in swarm intelligence: the case of ABC–FA and ABC–RUN algorithmsen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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