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dc.contributor.authorFalcón-Cardona, J.G.
dc.contributor.authorHernández Gómez, R.
dc.contributor.authorCoello, C.A. 
dc.contributor.authorCastillo Tapia, M.G.
dc.date.accessioned2022-01-02T12:24:55Z
dc.date.available2022-01-02T12:24:55Z
dc.date.issued2021-12-01
dc.identifier.issn22106502
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1405
dc.description.abstractMulti-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectParallel computingen_US
dc.titleParallel Multi-Objective Evolutionary Algorithms: A Comprehensive Surveyen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.swevo.2021.100960en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionen_US
dc.journal.titleSwarm and Evolutionary Computationen_US
dc.volume.number67en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España