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dc.contributor.authorOjalehto, Vesa
dc.contributor.authorNebro-Urbaneja, Antonio Jesus 
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorAldana-Montes, Jose Francisco 
dc.contributor.authorGarcía-Nieto, José
dc.contributor.authorBarba-González, Cristóbal
dc.date.accessioned2018-09-14T08:19:59Z
dc.date.available2018-09-14T08:19:59Z
dc.date.created2018
dc.date.issued2018-09-14
dc.identifier.urihttps://hdl.handle.net/10630/16463
dc.description.abstractOver the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization for guiding the generation of consecutive reference points, hence, replacing the decision maker in preference articulation. We use the artificial decision maker to compare interactive methods. We demonstrate the artificial decision maker using the DTLZ benchmark problems with 3, 5 and 7 objectives to compare R-NSGA-II and WASF-GA as interactive methods. The experimental results show that the proposed artificial decision maker is useful and efficient. It offers an intuitive and flexible mechanism to capture the current context when testing interactive methods for decision making.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectToma de decisionesen_US
dc.subject.otherMultiobjective optimizationen_US
dc.subject.otherPreference articulationen_US
dc.subject.otherMultiple criteria decision makingen_US
dc.subject.otherParticle swarm optimizationen_US
dc.titleArtificial decision maker driven by PSO: an approach for testing reference point based interactive methodsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitlePPSN 2018en_US
dc.relation.eventplaceCoimbra (Portugal)en_US
dc.relation.eventdateseptiembre 2018en_US


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