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dc.contributor.authorAghaei pour, Pouya
dc.contributor.authorHakanen, Jussi
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2024-06-06T06:27:08Z
dc.date.available2024-06-06T06:27:08Z
dc.date.issued2024
dc.identifier.citationAghaei pour, P., Hakanen, J., & Miettinen, K. (2024). A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems. <i>Journal of Global Optimization</i>, <i>Early online</i>. <a href="https://doi.org/10.1007/s10898-024-01387-z" target="_blank">https://doi.org/10.1007/s10898-024-01387-z</a>
dc.identifier.otherCONVID_216026073
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/95574
dc.description.abstractWe consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel surrogate-assisted evolutionary algorithm that can incorporate preference information given a priori. We employ Kriging models to approximate expensive objective and constraint functions, enabling us to introduce a new selection strategy that emphasizes the generation of feasible solutions throughout the optimization process. In our innovative model management, we perform expensive function evaluations to identify feasible solutions that best reflect the decision maker’s preferences provided before the process. To assess the performance of our proposed algorithm, we utilize two distinct parameterless performance indicators and compare them against existing algorithms from the literature using various real-world engineering and benchmark problems. Furthermore, we assemble new algorithms to analyze the effects of the selection strategy and the model management on the performance of the proposed algorithm. The results show that in most cases, our algorithm has a better performance than the assembled algorithms, especially when there is a restricted budget for expensive function evaluations.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesJournal of Global Optimization
dc.rightsCC BY 4.0
dc.subject.othermultiple objectives
dc.subject.othermodel management
dc.subject.othera priori algorithms
dc.subject.otherconstraint handling
dc.subject.othersurrogate-assisted optimization
dc.subject.otherconstrained problems
dc.subject.othercomputationally expensive problems
dc.titleA surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202406064334
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0925-5001
dc.relation.volumeEarly online
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2024
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311877
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoevoluutiolaskenta
dc.subject.ysoalgoritmit
dc.subject.ysokriging-menetelmä
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p28071
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p3126
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s10898-024-01387-z
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramProfilointi, SAfi
jyx.fundinginformationThis research was partly supported by the Academy of Finland (grant no 311877) and is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä. KAEA-C will be implemented in the open-source software DESDEO (https://desdeo.it.jyu.fi/) funded by the Academy of Finland. Open Access funding provided by University of Jyväskylä (JYU).
dc.type.okmA1


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