Large-scale truss-sizing optimization with enhanced hybrid HS algorithm

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dc.authorid0000-0001-8885-6468en_US
dc.contributor.authorDeğertekin, Sadık Özgür
dc.contributor.authorMinooei, Mohammad
dc.contributor.authorSantoro, Lorenzo
dc.contributor.authorTrentadue, Bartolomeo
dc.contributor.authorLamberti, Luciano
dc.date.accessioned2021-07-01T07:40:30Z
dc.date.available2021-07-01T07:40:30Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.descriptionWOS:000638345200001
dc.description.abstractAbstract: Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.en_US
dc.identifier.citationDeğertekin, S.Ö., Minooei, M., Santoro, L., Trentadue, B. ve Lamberti, L. (2021). Large-scale truss-sizing optimization with enhanced hybrid HS algorithm. Applied Sciences, 11(7), 1-34.en_US
dc.identifier.doi10.3390/app11073270
dc.identifier.endpage34en_US
dc.identifier.issn2076-3417
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85104250473
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.mdpi.com/2076-3417/11/7/3270
dc.identifier.urihttps://hdl.handle.net/11468/7188
dc.identifier.volume11en_US
dc.identifier.wosWOS:000638345200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorDeğertekin, Sadık Özgür
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofApplied Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHarmony searchen_US
dc.subjectJAYAen_US
dc.subjectLarge-scale structural optimizationen_US
dc.subjectTruss structuresen_US
dc.titleLarge-scale truss-sizing optimization with enhanced hybrid HS algorithmen_US
dc.titleLarge-scale truss-sizing optimization with enhanced hybrid HS algorithm
dc.typeArticleen_US

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