Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems

Basit Öğe Kaydı
dc.contributor.authorTurgut, Oguz Emrah
dc.contributor.authorTurgut, Mert Sinan
dc.date.accessioned2025-03-20T09:51:20Z
dc.date.available2025-03-20T09:51:20Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractThe Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the famous Runge-Kutta differential equation solver. Despite its relatively new emergence, this algorithm has several applications in various branches of scientific fields. However, there is still much room for improvement as it suffers from premature convergence resulting from inefficient search space exploration. To overcome this algorithmic drawback, this research study proposes a brand-new quasi-dynamic opposition-based learning (QDOPP) mechanism to be implemented in a standard Runge-Kutta optimizer to eliminate the local minimum points over the search space. Enhancing the asymmetric search hyperspace by taking advantage of various positions of the current solution within the domain is the critical novelty to enrich general diversity in the population, significantly improving the algorithm's overall exploration capability. To validate the effectivity of the proposed RUNGE-QDOPP method, thirty-four multidimensional optimization benchmark problems comprised of unimodal and multimodal test functions with various dimensionalities have been solved, and the corresponding results are compared against the predictions obtained from the other opposition-based learning variants as well as some state-of-art literature optimizers. Furthermore, six constrained engineering design problems with different functional characteristics have been solved, and the respective results are benchmarked against those obtained for the well-known optimizers. Comparison of the solution outcomes with literature optimizers for constrained and unconstrained test problems reveals that the proposed QDOPP has significant advantages over its counterparts regarding solution accuracy and efficiency.
dc.identifier.doi10.1007/s12065-024-00919-6
dc.identifier.endpage2962
dc.identifier.issn1864-5909
dc.identifier.issn1864-5917
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85187113669
dc.identifier.scopusqualityQ1
dc.identifier.startpage2899
dc.identifier.urihttps://doi.org/10.1007/s12065-024-00919-6
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2482
dc.identifier.volume17
dc.identifier.wosWOS:001179189000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEvolutionary Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectBenchmark functions
dc.subjectConstrained optimization
dc.subjectDynamic-opposite learning
dc.subjectOpposition-based learning
dc.subjectRunge-Kutta optimization algorithm
dc.titleQuasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu