Novel distance-fitness learning scheme for ameliorating metaheuristic optimization

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dc.authoridOliva, Diego/0000-0001-8781-7993
dc.authoridOZTURK, NIHAT/0000-0002-0607-1868
dc.authoridTejani, Ghanshyam/0000-0001-9106-0313;
dc.contributor.authorCelik, Emre
dc.contributor.authorHoussein, Essam H.
dc.contributor.authorAbdel-Salam, Mahmoud
dc.contributor.authorOliva, Diego
dc.contributor.authorTejani, Ghanshyam G.
dc.contributor.authorOzturk, Nihat
dc.contributor.authorSharma, Sunil Kumar
dc.date.accessioned2025-10-11T20:48:33Z
dc.date.available2025-10-11T20:48:33Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAn important portion of metaheuristic algorithms is guided by the fittest solution obtained so far. Searching around the fittest solution is beneficial for speeding up convergence, but it is detrimental considering local minima stagnation and premature convergence. A novel distance-fitness learning (DFL) scheme that provides better searchability and greater diversity is proposed to resolve these. The method allows search agents in the population to actively learn from the fittest solution, the worst solution, and an optimum distance-fitness (ODF) candidate. This way, it aims at approaching both the fittest solution and ODF candidate while at the same time moving away from the worst solution. The effectiveness of our proposal is evaluated by integrating it with the reptile search algorithm (RSA), which is an interesting algorithm that is simple to code but suffers from stagnating in local minima, converging too early, and a lack of sufficient global searchability. Empirical results from solving 23 standard benchmark functions, 10 Congresses on Evolutionary Computation (CEC) 2020 test functions, and 2 real-world engineering problems reveal that DFL boosts the capability of RSA significantly. Further, the comparison of DFL-RSA with popular algorithms vividly signifies the potential and superiority of the method over most of the problems in terms of solution precision.en_US
dc.description.sponsorshipDeanship of Postgraduate Studies and Scientific Research at Majmaah University [R-2025-1669]en_US
dc.description.sponsorshipThe author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (R-2025-1669) .en_US
dc.identifier.doi10.1016/j.jestch.2025.102053
dc.identifier.issn2215-0986
dc.identifier.scopus2-s2.0-105001303443en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2025.102053
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21962
dc.identifier.volume65en_US
dc.identifier.wosWOS:001459999700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltden_US
dc.relation.ispartofEngineering Scienceand Technology-An International Journal-Jestechen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectMetaheuristicen_US
dc.subjectReptile search algorithmen_US
dc.subjectDistance-fitness learningen_US
dc.subjectGlobal optimizationen_US
dc.titleNovel distance-fitness learning scheme for ameliorating metaheuristic optimizationen_US
dc.typeArticleen_US

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