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dc.contributor.authorTabatabaei, Mohammad
dc.contributor.authorLovison, Alberto
dc.contributor.authorTan, Matthias
dc.contributor.authorHartikainen, Markus
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2018-12-18T05:18:30Z
dc.date.available2018-12-18T05:18:30Z
dc.date.issued2018
dc.identifier.citationTabatabaei, M., Lovison, A., Tan, M., Hartikainen, M., & Miettinen, K. (2018). ANOVA-MOP : ANOVA Decomposition for Multiobjective Optimization. <i>SIAM Journal on Optimization</i>, <i>28</i>(4), 3260-3289. <a href="https://doi.org/10.1137/16M1096505" target="_blank">https://doi.org/10.1137/16M1096505</a>
dc.identifier.otherCONVID_28791093
dc.identifier.otherTUTKAID_79920
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60635
dc.description.abstractReal-world optimization problems may involve a number of computationally expensive functions with a large number of input variables. Metamodel-based optimization methods can reduce the computational costs of evaluating expensive functions, but this does not reduce the dimension of the search domain nor mitigate the curse of dimensionality effects. The dimension of the search domain can be reduced by functional anova decomposition involving Sobol' sensitivity indices. This approach allows one to rank decision variables according to their impact on the objective function values. On the basis of the sparsity of effects principle, typically only a small number of decision variables significantly affects an objective function. Therefore, neglecting the variables with the smallest impact should lead to an acceptably accurate and simpler metamodel for the original problem. This appealing strategy has been applied only to single-objective optimization problems so far. Given a high-dimensional optimization problem with multiple objectives, a method called anova-mop is proposed for defining a number of independent low-dimensional subproblems with a smaller number of objectives. The method allows one to define approximated solutions for the original problem by suitably combining the solutions of the subproblems. The quality of the approximated solutions and both practical and theoretical aspects related to decision making are discussed.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSociety for Industrial and Applied Mathematics
dc.relation.ispartofseriesSIAM Journal on Optimization
dc.rightsIn Copyright
dc.subject.othermultiple criteria optimization
dc.subject.othersensitivity analysis
dc.subject.othermetamodeling
dc.subject.otherdimensionality reduction
dc.subject.otherPareto optimality
dc.titleANOVA-MOP : ANOVA Decomposition for Multiobjective Optimization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201812175160
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.updated2018-12-17T10:15:31Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange3260-3289
dc.relation.issn1052-6234
dc.relation.numberinseries4
dc.relation.volume28
dc.type.versionacceptedVersion
dc.rights.copyright© 2018, Society for Industrial and Applied Mathematics.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber287496
dc.subject.ysopareto-tehokkuus
dc.subject.ysopäätöksenteko
dc.subject.ysomonitavoiteoptimointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28039
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1137/16M1096505
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThis work was partly funded by the Academy of Finland Project 287496, Early Career Scheme (ECS) Project 21201414, the General Research Fund Project 11226716, and sponsored by the Research Grants Council of Hong Kong and the KAUTE Foundation.


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