Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1543
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dc.contributor.authorYibre, Abdulkerim Mohammed-
dc.contributor.authorKoçer, Barış-
dc.contributor.authorEsleman, Esmael Adem-
dc.contributor.authorÖnal, Gürol-
dc.date.accessioned2021-12-13T10:41:30Z-
dc.date.available2021-12-13T10:41:30Z-
dc.date.issued2020-
dc.identifier.issn2523-3963-
dc.identifier.issn2523-3971-
dc.identifier.urihttps://doi.org/10.1007/s42452-020-3126-0-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1543-
dc.description.abstractThe optimal design of parameters is vital for the effective use of hybrid composite laminated structures. This is due to a highly dependent property of laminated composite structures strength on its fiber orientation, stacking sequence and the number of ply in each laminate. The main aim of this study is to apply Learning-Oriented Artificial Algae Algorithm for optimization of the weight of rectangular hybrid composite laminated plate subjected to compressive in-plane loading. The design parameters are number of plies and stacking sequence of the laminate. The critical buckling factor is the constraint of the optimization process. The parameters of the hybrid composite plate are optimized using Learning-Oriented Artificial Algae Algorithm with the aim of minimizing weight.The performance of the algorithm was compared with previous studies that employed the GA and ACO algorithms. The Learning-Oriented method is integrated to reduce the number of functions evaluated and in turn reducing computational cost. The results showed that Learning-Oriented Artificial Algae Algorithm outperformed GA and ACO, and hence can be successfully applied in the optimization of laminated composite structures.en_US
dc.language.isoenen_US
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AGen_US
dc.relation.ispartofSN APPLIED SCIENCESen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Algae Algorithmen_US
dc.subjectBuckling Loaden_US
dc.subjectHybrid Composite Laminateen_US
dc.subjectWeight Optimizationen_US
dc.subjectStacking Sequenceen_US
dc.subjectDesign Optimizationen_US
dc.subjectBuckling Optimizationen_US
dc.subjectStacking-Sequenceen_US
dc.subjectGenetic Algorithmen_US
dc.subjectPlatesen_US
dc.subjectLoaden_US
dc.titleWeight optimization of hybrid composite laminate using learning-oriented artificial algae algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s42452-020-3126-0-
dc.identifier.scopus2-s2.0-85100776776en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authoridEsleman, Esmael Adem/0000-0003-0769-2487-
dc.authorwosidEsleman, Esmael Adem/AAP-7912-2020-
dc.identifier.volume2en_US
dc.identifier.issue8en_US
dc.identifier.wosWOS:000548338400002en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57210369920-
dc.authorscopusid35786168500-
dc.authorscopusid57221946043-
dc.authorscopusid8605606500-
dc.identifier.scopusquality--
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.10. Department of Mechanical Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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