Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69494
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dc.contributor.authorMéndez Babey, Máximoen_US
dc.contributor.authorRossit, Daniel A.en_US
dc.contributor.authorGonzález Landín, Begoñaen_US
dc.contributor.authorFrutos, Marianoen_US
dc.date.accessioned2020-01-30T10:05:51Z-
dc.date.available2020-01-30T10:05:51Z-
dc.date.issued2020en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10553/69494-
dc.description.abstractThis paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Accessen_US
dc.sourceIEEE Access [ISSN 2169-3536], v. 8, p. 3482 - 3497en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherDifferential evolutionen_US
dc.subject.otherEvolutionary computationen_US
dc.subject.otherGear train optimizationen_US
dc.subject.otherGenetic algorithmsen_US
dc.subject.otherMechanical engineeringen_US
dc.subject.otherMulti-objective evolutionary algorithmsen_US
dc.subject.otherNon-dominated sorting genetic algorithm-IIen_US
dc.titleProposal and comparative study of evolutionary algorithms for optimum design of a gear systemen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2019.2962906en_US
dc.identifier.scopus85078416731-
dc.contributor.authorscopusid23474473600-
dc.contributor.authorscopusid57204662073-
dc.contributor.authorscopusid55643744700-
dc.contributor.authorscopusid24482935700-
dc.description.lastpage3497en_US
dc.description.firstpage3482en_US
dc.relation.volume8en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,587
dc.description.jcr3,367
dc.description.sjrqQ1
dc.description.jcrqQ2
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR SIANI: Computación Evolutiva y Aplicaciones-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR SIANI: Computación Evolutiva y Aplicaciones-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.orcid0000-0002-7133-7108-
crisitem.author.orcid0000-0002-7915-0655-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameMéndez Babey, Máximo-
crisitem.author.fullNameGonzález Landín, Begoña-
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