Global best-guided oppositional algorithm for solving multidimensional optimization problems

Basit Öğe Kaydı
dc.authoridTurgut, Oguz Emrah / 0000-0003-3556-8889
dc.authorscopusid56228320400
dc.authorscopusid57200158463
dc.contributor.authorTurgut, Mert Sinan
dc.contributor.authorTurgut, Oğuz Emrah
dc.date.accessioned2022-02-15T16:58:44Z
dc.date.available2022-02-15T16:58:44Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractThis paper presents an alternative optimization algorithm to the literature optimizers by introducing global best-guided oppositional-based learning method. The procedure at hand uses the active and recent manipulation schemes of oppositional learning procedure by applying some modifications to them. The first part of the algorithm deals with searching the optimum solution around the current best solution by means of the ensemble learning-based strategy through which unfeasible and semi-optimum solutions have been straightforwardly eliminated. The second part of the algorithm benefits the useful merits of the quasi-oppositional learning strategy to not only improve the solution diversity but also enhance the convergence speed of the whole algorithm. A set of 22 optimization benchmark functions have been solved and corresponding results have been compared with the outcomes of the well-known literature optimization algorithms. Then, a bunch of parameter estimation problem consisting of hard-to-solve real world applications has been analyzed by the proposed method. Following that, eight widely applied constrained benchmark problems along with well-designed 12 constrained test cases proposed in CEC 2006 session have been solved and evaluated in terms of statistical analysis. Finally, a heat exchanger design problem taken from literature study has been solved through the proposed algorithm and respective solutions have been benchmarked against the prevalent optimization algorithms. Comparison results show that optimization procedure dealt with in this study is capable of achieving the utmost performance in solving multidimensional optimization algorithms.en_US
dc.identifier.doi10.1007/s00366-018-0684-5
dc.identifier.endpage73en_US
dc.identifier.issn0177-0667
dc.identifier.issn1435-5663
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85059527769en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage43en_US
dc.identifier.urihttps://doi.org/10.1007/s00366-018-0684-5
dc.identifier.urihttps://hdl.handle.net/20.500.14034/459
dc.identifier.volume36en_US
dc.identifier.wosWOS:000520227700004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.journalEngineering With Computersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHeat exchanger designen_US
dc.subjectMultidimensional optimizationen_US
dc.subjectOppositional-based learningen_US
dc.subjectParameter estimationen_US
dc.subjectStochastic searchen_US
dc.subjectTube Heat-Exchangersen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectEconomic Optimizationen_US
dc.subjectParameter-Identificationen_US
dc.subjectDesign Optimizationen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectDifferential Evolutionen_US
dc.subjectHarmony Searchen_US
dc.subjectMathematical-Modelsen_US
dc.subjectFirefly Algorithmen_US
dc.titleGlobal best-guided oppositional algorithm for solving multidimensional optimization problemsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Global best-guided oppositional algorithm for solving multidimensional optimization problems.pdf
Boyut:
4 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Endüstri Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu