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dc.contributor.authorMoya Señas, Ignacio 
dc.contributor.authorChica Serrano, Manuel 
dc.contributor.authorCordón García, Óscar 
dc.date.accessioned2021-05-07T09:09:37Z
dc.date.available2021-05-07T09:09:37Z
dc.date.issued2021-04-02
dc.identifier.citationI. Moya et al.: EMO for Automatic ABM Calibration, IEEE ACCESS, VOLUME 9, 2021, 55284-55299. DOI: [10.1109/ACCESS.2021.3070071]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/68382
dc.descriptionThis work was supported by the Spanish Agencia Estatal de Investigacion, the Andalusian Government, the University of Granada, and European Regional Development Funds (ERDF) under Grants EXASOCO (PGC2018-101216-B-I00), SIMARK (P18-TP-4475), and AIMAR (A-TIC-284-UGR18). Manuel Chica was also supported by the Ramon y Cajal program (RYC-2016-19800).es_ES
dc.descriptionThe authors would like to thank the ``Centro de Servicios de Informática y Redes de Comunicaciones'' (CSIRC), University of Granada, for providing the computing resources (Alhambra supercomputer).es_ES
dc.description.abstractComplex problems can be analyzed by using model simulation but its use is not straight-forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. We propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent-based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e., the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.es_ES
dc.description.sponsorshipSpanish Agencia Estatal de Investigaciones_ES
dc.description.sponsorshipAndalusian Governmentes_ES
dc.description.sponsorshipUniversity of Granadaes_ES
dc.description.sponsorshipEuropean Commission PGC2018-101216-B-I00 P18-TP-4475 A-TIC-284-UGR18es_ES
dc.description.sponsorshipSpanish Government RYC-2016-19800es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectModel calibrationes_ES
dc.subjectAgent-Based Modelinges_ES
dc.subjectEvolutionary multiobjective optimizationes_ES
dc.titleEvolutionary multiobjective optimization for automatic agent-based model calibration: A comparative studyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1109/ACCESS.2021.3070071
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España