Fitness-Distance-Constraint (FDC) based guide selection method for constrained optimization problems

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dc.authoridOzkaya, Burcin/0000-0002-9858-3982en_US
dc.authoridKAHRAMAN, Hamdi Tolga/0000-0001-9985-6324en_US
dc.authoridguvenc, ugur/0000-0002-5193-7990en_US
dc.authoridDuman, Serhat/0000-0002-1091-125Xen_US
dc.authorscopusid57199648907en_US
dc.authorscopusid23389512500en_US
dc.authorscopusid35101845300en_US
dc.authorscopusid25651286200en_US
dc.authorwosidOzkaya, Burcin/KLD-8092-2024en_US
dc.authorwosidKAHRAMAN, Hamdi Tolga/AAW-5335-2020en_US
dc.authorwosidDuman, Serhat/O-9406-2014en_US
dc.authorwosidguvenc, ugur/H-3029-2011en_US
dc.contributor.authorOzkaya, Burcin
dc.contributor.authorKahraman, Hamdi Tolga
dc.contributor.authorDuman, Serhat
dc.contributor.authorGuvenc, Ugur
dc.date.accessioned2024-08-23T16:04:57Z
dc.date.available2024-08-23T16:04:57Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractIn the optimization of constrained type problems, the main difficulty is the elimination of the constraint violations in the evolutionary search process. Evolutionary algorithms are designed by default according to the requirements of unconstrained and continuous global optimization problems. Since there are no constraint functions in these type of problems, the constraint violations are not considered in the design of the guiding mechanism of evolutionary algorithms. In this study, two new methods were introduced to redesign the evolutionary algorithms in accordance with the requirements of constrained optimization problems. These were (i) constraint space-based, called Fitness-Distance -Constraint (FDC), selection method and (ii) dynamic guiding mechanism. Firstly, thanks to the FDC guide selection method, the constraint violation values of the individuals in the population were converted into score values and the individuals who increase the diversity in the search process were selected as guide. On the other hand, in dynamic guiding mechanism, the FDC method was applied in case of constraint violation, otherwise the default guide selection method was used The proposed methods were used to redesign the guiding mechanism of adaptive guided differential evolution (AGDE), a current evolutionary algorithm, and the FDC-AGDE algorithm was designed. The performance of the FDC-AGDE was tested on eleven different constrained real-world optimization problems. The results of the FDC-AGDE and AGDE were evaluated using the Friedman and Wilcoxon test methods. According to Wilcoxon pairwise results, the FDC-AGDE showed better performance than the AGDE in nine of the eleven problems and equal performance in two of the eleven problems. Moreover, the proposed algorithm was compared with the competitive and up-to-date MHS algorithms in terms of the results of Friedman test, Wilcoxon test, feasibility rate, and success rate. According to Friedman test results, the first three algorithms were the FDC-AGDE, LSHADE-SPACMA, and AGDE algorithms with the score of 2.69, 4.05, and 4.34, respectively. According to the mean values of the success rates obtained from the eleven problems, the FDC-AGDE, LSHADE-SPACMA, and AGDE algorithms ranked in the first three with the success rates of 67%, 48% and 28%, respectively. Consequently, the FDC-AGDE algorithm showed a superior performance comparing with the competing MHS algorithms. According to the results, it is expected that the proposed methods will be widely used in the constrained optimization problems in the future.& COPY; 2023 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2023.110479
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85162147766en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2023.110479
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14418
dc.identifier.volume144en_US
dc.identifier.wosWOS:001054902400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFitness-Distance-Constraint (FDC)en_US
dc.subjectMeta-heuristic search algorithm designen_US
dc.subjectOptimizationen_US
dc.subjectConstrained optimization problemen_US
dc.subjectDynamic Economic-Dispatchen_US
dc.subjectDifferential Evolution Algorithmen_US
dc.subjectReactive Power Dispatchen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectHandling Methoden_US
dc.subjectLoad Dispatchen_US
dc.subjectSearchen_US
dc.subjectFlowen_US
dc.subjectAdaptationen_US
dc.subjectStrategiesen_US
dc.titleFitness-Distance-Constraint (FDC) based guide selection method for constrained optimization problemsen_US
dc.typeArticleen_US

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