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dc.contributor.authorTabatabaei, Mohammad
dc.contributor.authorHartikainen, Markus
dc.contributor.authorSindhya, Karthik
dc.contributor.authorHakanen, Jussi
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2019-05-24T05:52:48Z
dc.date.available2019-05-24T05:52:48Z
dc.date.issued2019
dc.identifier.citationTabatabaei, M., Hartikainen, M., Sindhya, K., Hakanen, J., & Miettinen, K. (2019). An interactive surrogate-based method for computationally expensive multiobjective optimisation. <i>Journal of the Operational Research Society</i>, <i>70</i>(6), 898-914. <a href="https://doi.org/10.1080/01605682.2018.1468860" target="_blank">https://doi.org/10.1080/01605682.2018.1468860</a>
dc.identifier.otherCONVID_28059820
dc.identifier.otherTUTKAID_77678
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64164
dc.description.abstractMany disciplines involve computationally expensive multiobjective optimisation problems. Surrogate-based methods are commonly used in the literature to alleviate the computational cost. In this paper, we develop an interactive surrogate-based method called SURROGATE-ASF to solve computationally expensive multiobjective optimisation problems. This method employs preference information of a decision-maker. Numerical results demonstrate that SURROGATE-ASF efficiently provides preferred solutions for a decision-maker. It can handle different types of problems involving for example multimodal objective functions and nonconvex and/or disconnected Pareto frontiers.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherPalgrave Macmillan Ltd.
dc.relation.ispartofseriesJournal of the Operational Research Society
dc.rightsCC BY-NC-ND 4.0
dc.subject.othermultiple criteria decision-making (MCDM)
dc.subject.otherinteractive methods
dc.subject.othercomputational cost
dc.subject.otherblack-box functions
dc.subject.othermetamodeling techniques
dc.subject.otherachievement scalarising function
dc.titleAn interactive surrogate-based method for computationally expensive multiobjective optimisation
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201905222733
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-05-22T12:15:22Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange898-914
dc.relation.issn0160-5682
dc.relation.numberinseries6
dc.relation.volume70
dc.type.versionpublishedVersion
dc.rights.copyright© 2018 Operational Research Society
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber287496
dc.subject.ysomatemaattinen optimointi
dc.subject.ysomonitavoiteoptimointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p17635
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1080/01605682.2018.1468860
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThis work was partly funded by the COMAS Doctoral Program at the University of Jyvaskyla, the Academy of Finland [project No. 287496], Early Career Scheme (ECS) sponsored by the Research Grants Council of Hong Kong [project No. 21201414 (Dr. Matthias Hwai Yong Tan)] and the KAUTE Foundation.


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Except where otherwise noted, this item's license is described as CC BY-NC-ND 4.0