dc.contributor.author | Chugh, Tinkle | |
dc.contributor.author | Kratky, Tomas | |
dc.contributor.author | Miettinen, Kaisa | |
dc.contributor.author | Jin, Yaochu | |
dc.contributor.author | Makkonen, Pekka | |
dc.date.accessioned | 2019-07-22T08:35:22Z | |
dc.date.available | 2019-07-22T08:35:22Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Chugh, 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.other | CONVID_30675224 | |
dc.identifier.other | TUTKAID_81443 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/65085 | |
dc.description.abstract | We 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.extent | 1545 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | ACM | |
dc.relation.ispartof | GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference | |
dc.rights | In Copyright | |
dc.subject.other | evolutionary multi-objective optimization | |
dc.subject.other | optimal shape design | |
dc.subject.other | computational costs | |
dc.subject.other | metamodels | |
dc.subject.other | multiple criteria decision making | |
dc.subject.other | Pareto optimality | |
dc.subject.other | preference information | |
dc.title | Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-201907183649 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Laskennallinen tiede | fi |
dc.contributor.oppiaine | Multiobjective Optimization Group | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.contributor.oppiaine | Computational Science | en |
dc.contributor.oppiaine | Multiobjective Optimization Group | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.date.updated | 2019-07-18T12:15:12Z | |
dc.relation.isbn | 978-1-4503-6111-8 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 1147-1155 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2019 Association for Computing Machinery | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | Genetic and Evolutionary Computation Conference | |
dc.relation.grantnumber | 40147/14,1570/31/201 | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | muoto | |
dc.subject.yso | mallintaminen | |
dc.subject.yso | monitavoiteoptimointi | |
dc.subject.yso | pareto-tehokkuus | |
dc.subject.yso | ilmanvaihtojärjestelmät | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p7250 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p3533 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p32016 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p28039 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p19042 | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.1145/3321707.3321745 | |
dc.relation.funder | TEKES | fi |
dc.relation.funder | TEKES | en |
jyx.fundingprogram | Muut, TEKES | fi |
jyx.fundingprogram | Others, TEKES | en |
jyx.fundinginformation | This 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.okm | A4 | |