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dc.contributor.authorChugh, Tinkle
dc.contributor.authorKratky, Tomas
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorJin, Yaochu
dc.contributor.authorMakkonen, Pekka
dc.date.accessioned2019-07-22T08:35:22Z
dc.date.available2019-07-22T08:35:22Z
dc.date.issued2019
dc.identifier.citationChugh, T., Kratky, T., Miettinen, K., Jin, Y., & Makkonen, P. (2019). Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm. In <i>GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference</i> (pp. 1147-1155). ACM. <a href="https://doi.org/10.1145/3321707.3321745" target="_blank">https://doi.org/10.1145/3321707.3321745</a>
dc.identifier.otherCONVID_30675224
dc.identifier.otherTUTKAID_81443
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/65085
dc.description.abstractWe formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. We are motivated by practical applicability and focus on two main challenges faced by practitioners in industry: 1) meaningful formulation of the optimization problem reflecting the needs of a decision maker and 2) finding a desirable solution based on a decision maker’s preferences when solving a problem with computationally expensive function evaluations. For the first challenge, we describe the procedure of modelling a component in the air intake ventilation system with commercial simulation tools. The problem to be solved involves time consuming computational fluid dynamics simulations. Therefore, for the second challenge, we extend a recently proposed Kriging-assisted evolutionary algorithm K-RVEA to incorporate a decision maker’s preferences. Our numerical results indicate efficiency in using the computing resources available and the solutions obtained reflect the decision maker’s preferences well. Actually, two of the solutions dominate the baseline design (the design provided by the decision maker before the optimization process). The decision maker was satisfied with the results and eventually selected one as the final solution.fi
dc.format.extent1545
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofGECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference
dc.rightsIn Copyright
dc.subject.otherevolutionary multi-objective optimization
dc.subject.otheroptimal shape design
dc.subject.othercomputational costs
dc.subject.othermetamodels
dc.subject.othermultiple criteria decision making
dc.subject.otherPareto optimality
dc.subject.otherpreference information
dc.titleMultiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201907183649
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-07-18T12:15:12Z
dc.relation.isbn978-1-4503-6111-8
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1147-1155
dc.type.versionacceptedVersion
dc.rights.copyright© 2019 Association for Computing Machinery
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceGenetic and Evolutionary Computation Conference
dc.relation.grantnumber40147/14,1570/31/201
dc.subject.ysokoneoppiminen
dc.subject.ysomuoto
dc.subject.ysomallintaminen
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysopareto-tehokkuus
dc.subject.ysoilmanvaihtojärjestelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p7250
jyx.subject.urihttp://www.yso.fi/onto/yso/p3533
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p28039
jyx.subject.urihttp://www.yso.fi/onto/yso/p19042
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1145/3321707.3321745
dc.relation.funderTEKESfi
dc.relation.funderTEKESen
jyx.fundingprogramMuut, TEKESfi
jyx.fundingprogramOthers, TEKESen
jyx.fundinginformationThis work was partly funded by TEKES, the Finnish Funding Agency for Innovation under the FiDiPro project DeCoMo and the Natural Environment Research Council [grant number NE/P017436/1]. We would also like to thank Valtra Inc. for providing the problem. This research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyvaskyla.
dc.type.okmA4


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