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dc.contributor.authorAghaei Pour, Pouya
dc.contributor.authorRodemann, Tobias
dc.contributor.authorHakanen, Jussi
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2021-01-14T13:43:33Z
dc.date.available2021-01-14T13:43:33Z
dc.date.issued2021
dc.identifier.citationAghaei Pour, P., Rodemann, T., Hakanen, J., & Miettinen, K. (2021). Surrogate assisted interactive multiobjective optimization in energy system design of buildings. <i>Optimization and Engineering</i>, <i>Early online</i>. <a href="https://doi.org/10.1007/s11081-020-09587-8" target="_blank">https://doi.org/10.1007/s11081-020-09587-8</a>
dc.identifier.otherCONVID_47501927
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/73621
dc.description.abstractIn this paper, we develop a novel evolutionary interactive method called interactive K-RVEA, which is suitable for computationally expensive problems. We use surrogate models to replace the original expensive objective functions to reduce the computation time. Typically, in interactive methods, a decision maker provides some preferences iteratively and the optimization algorithm narrows the search according to those preferences. However, working with surrogate model swill introduce some inaccuracy to the preferences, and therefore, it would be desirable that the decision maker can work with the solutions that are evaluated with the original objective functions. Therefore, we propose a novel model management strategy to incorporate the decision maker’s preferences to select some of the solutions for both updating the surrogate models (to improve their accuracy) and to show them to the decision maker. Moreover, we solve a simulation-based computationally expensive optimization problem by finding an optimal configuration for an energy system of a heterogeneous business building complex. We demonstrate how a decision maker can interact with the method and how the most preferred solution is chosen.Finally, we compare our method with another interactive method, which does not have any model management strategy, and shows how our model management strategy can help the algorithm to follow the decision maker’s preferences.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesOptimization and Engineering
dc.rightsCC BY 4.0
dc.subject.othermodel management
dc.subject.otherevolutionary interactive methods
dc.subject.othersurrogate-assisted optimization
dc.subject.othermultiobjective optimization
dc.subject.othercomputationally expensive problems
dc.titleSurrogate assisted interactive multiobjective optimization in energy system design of buildings
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202101141097
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.description.reviewstatuspeerReviewed
dc.relation.issn1389-4420
dc.relation.volumeEarly online
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2021
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoenergiajärjestelmät
dc.subject.ysoLVI-suunnittelu
dc.subject.ysopäätöksentukijärjestelmät
dc.subject.ysorakennussuunnittelu
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p22348
jyx.subject.urihttp://www.yso.fi/onto/yso/p10463
jyx.subject.urihttp://www.yso.fi/onto/yso/p27803
jyx.subject.urihttp://www.yso.fi/onto/yso/p6308
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s11081-020-09587-8
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationThis work was partly supported by Honda Research Institute Europe. This research was partly supported bythe Academy of Finland (grant no 311877) and is related to the thematic research area DEMO (Decision An-alytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyv ̈askyl ̈a.


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