A high-level and adaptive metaheuristic selection algorithm for solving high dimensional bound-constrained continuous optimization problems

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dc.contributor.authorUğur, Aybars
dc.contributor.authorGökalp, Osman
dc.date.accessioned2023-01-12T20:32:00Z
dc.date.available2023-01-12T20:32:00Z
dc.date.issued2020
dc.departmentN/A/Departmenten_US
dc.description.abstractMetaheuristic algorithms are used to find sufficiently good solutions for the optimization problems thatare not solvable in a polynomial time. Although metaheuristics offer a general problem-solving framework and canbe applied to various types of optimization problems, their performances depend heavily on the problem to be solved.Thus, hybrid metaheuristics are used to combine strong parts of different algorithms. In this study, a novel adaptivemetaheuristic selection algorithm is proposed for solving bound-constrained continuous optimization problems. Thedeveloped method hybridizes artificial bee colony, differential evolution, and particle swarm optimization at a high levelwhere each algorithm works independently from each other. As a main contribution to the literature, adaptive selectionat metaheuristic level among these three algorithms is achieved by using a rank-based credit assignment and UCB1multiarmed bandit selection. The effectiveness of the developed algorithm has been evaluated on CEC’17 standardbenchmark functions. The obtained numerical results indicate that the proposed algorithm outperforms the individualmetaheuristics on which it is built and is more effective especially in high dimensional problems. It is also shown thatthe proposed algorithm is highly comparable with the related algorithms in the literature. Lastly, a case study thatachieves adaptive selection of two good-performing algorithms (namely, covariance matrix adaptation evolution strategyand JADE) for the benchmark used in this study supports the effectiveness of the proposed method.en_US
dc.identifier.doi10.3906/elk-1908-9
dc.identifier.endpage1566en_US
dc.identifier.issn1300-0632
dc.identifier.issn1300-0632
dc.identifier.issue3en_US
dc.identifier.startpage1549en_US
dc.identifier.trdizinid338451en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1908-9
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/338451
dc.identifier.urihttps://hdl.handle.net/11454/80983
dc.identifier.volume28en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleA high-level and adaptive metaheuristic selection algorithm for solving high dimensional bound-constrained continuous optimization problemsen_US
dc.typeArticleen_US

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