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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.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.contributor.oppiaineComputational Scienceen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-05-22T12:15:22Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
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.funderResearch Council 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.
dc.type.okmA1


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CC BY-NC-ND 4.0
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