A powerful variant of symbiotic organisms search algorithm for global optimization

Basit Öğe Kaydı
dc.contributor.authorCelik, Emre
dc.date.accessioned2021-12-01T18:47:17Z
dc.date.available2021-12-01T18:47:17Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractThis paper suggests a new variation to the existing symbiotic organisms search (SOS) algorithm developed by simulating three symbiotic strategies of mutualism, commensalism and parasitism used by the organisms. In the revised version called improved SOS (ISOS), the theory of quasi-oppositional based learning is employed during generation of initial population and in the parasitism phase to raise the possibility of getting closer to high-quality solutions. An efficient alternative for parasitism phase is also presented. The two upgraded parasitism strategies avoid the over exploration issue of original parasitism phase that causes unwanted longtime search in the inferior search space as the solution is already refined. To guide the algorithm perform an exhaustive search around the best solution in attempting to further improve the search model of ISOS, a chaotic local search based on the piecewise linear chaotic map is coupled into the proposed algorithm. Twentysix benchmark functions and three engineering design problems are tested and a contrast with other popular metaheuristics is widely established. Comparative results substantiate the great contribution of proposed ISOS algorithm in solving various optimization problems with superior global search capability and convergence characteristics which render it useful in handling global optimization problems.en_US
dc.identifier.doi10.1016/j.engappai.2019.103294
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85073531238en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2019.103294
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10215
dc.identifier.volume87en_US
dc.identifier.wosWOS:000506715100030en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorCelik, Emre
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications Of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSymbiotic organisms searchen_US
dc.subjectQuasi-oppositional based learningen_US
dc.subjectChaotic theoryen_US
dc.subjectLocal searchen_US
dc.subjectBenchmark functionen_US
dc.subjectEngineering designen_US
dc.subjectGlobal optimizationen_US
dc.subjectAutomatic Voltage Regulatoren_US
dc.subjectHybrid Genetic Algorithmen_US
dc.subjectPid Controlleren_US
dc.subjectPerformance Analysisen_US
dc.subjectEfficient Designen_US
dc.subjectOppositionen_US
dc.titleA powerful variant of symbiotic organisms search algorithm for global optimizationen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
10215.pdf
Boyut:
1.3 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu