Publication:
Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame

dc.contributor.authorPanagant, Natee
dc.contributor.authorPholdee, Nantiwat
dc.contributor.authorWansasueb, Kittinan
dc.contributor.authorBureerat, Sujin
dc.contributor.authorSait, Sadiq M.
dc.contributor.buuauthorYıldız, Ali Rıza
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentMakina Mühendisliği
dc.contributor.departmentKonstrüksiyon ve İmalat Bölümü
dc.contributor.researcheridF-7426-2011
dc.contributor.scopusid7102365439
dc.date.accessioned2024-02-13T06:33:27Z
dc.date.available2024-02-13T06:33:27Z
dc.date.issued2019
dc.description.abstractIn this paper, an approach called real-code population-based incremental learning hybridised with adaptive differential evolution (RPBILADE) is proposed for solving many-objective automotive floor-frame optimisation problems. Adaptive strategies are developed and integrated into the algorithm. The purpose of these strategies is to select suitable control parameters for each stage of an optimisation run, in order to improve the search performance and consistency of the algorithm. The automotive floor-frame structures are considered as frame structures that can be analysed with finite element analysis. The design variables of the problems include topology, shape, and size. Ten optimisation runs using various optimisers are carried out on two many-objective automotive floor-frame optimisation problems. Twelve additional benchmark tests against all competitors are also performed to demonstrate the search performance of the proposed algorithm. RPBILADE provided better results than other recent optimisers for both the automotive floor-frame optimisation and benchmark problems.
dc.description.sponsorshipThailand Research Fund (TRF) -- RTA6180010
dc.identifier.citationPanagant, N. vd. (2019). ''Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame''. International Journal of Vehicle Desing, 80(2-4), Special Issue, 176-208.
dc.identifier.endpage208
dc.identifier.issn0143-3369
dc.identifier.issn1741-5314
dc.identifier.issue2-4
dc.identifier.pubmed
dc.identifier.scopus2-s2.0-85092306246
dc.identifier.startpage176
dc.identifier.urihttps://doi.org/10.1504/IJVD.2019.109863
dc.identifier.urihttps://hdl.handle.net/11452/39644
dc.identifier.volume80
dc.identifier.wos000576400300006
dc.indexed.scopusScopus
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherInderscience Enterprises
dc.relation.collaborationYurt dışı
dc.relation.collaborationSanayi
dc.relation.journalInternational Journal of Vehicle Desing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectTransportation
dc.subjectEngineering
dc.subjectAutomotive floor-frame design
dc.subjectMany-objective optimisation
dc.subjectPopulation-based
dc.subjectIncremental learning
dc.subjectDifferential evolution
dc.subjectAdaptive algorithm
dc.subjectNondominated sorting approach
dc.subjectDifferential evolution
dc.subjectMultiobjective optimization
dc.subjectTopology optimization
dc.subjectMultiple objectives
dc.subjectGenetic algorithm
dc.subjectWater cycle
dc.subjectGrey wolf
dc.subjectAnt lion
dc.subjectDesing
dc.subjectBenchmarking
dc.subjectEvolutionary algorithms
dc.subjectOptimization
dc.subjectStructural frames
dc.subjectAdaptive differential evolutions
dc.subjectAdaptive strategy
dc.subjectBench-mark problems
dc.subjectControl parameters
dc.subjectObjective optimisation
dc.subjectOptimisation problems
dc.subjectPopulation based incremental learning
dc.subjectSearch performance
dc.subjectFloors
dc.subject.scopusDecomposition; Evolutionary Multiobjective Optimization; Pareto Front
dc.subject.wosEngineering, mechanical
dc.subject.wosTransportation science & technology
dc.titleComparison of recent algorithms for many-objective optimisation of an automotive floor-frame
dc.typeArticle
dc.wos.quartileQ3
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Makina Mühendisliği/Konstrüksiyon ve İmalat Bölümü
local.indexed.atWOS
local.indexed.atScopus

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