Bilevel optimization by conditional Bayesian optimization

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dc.contributor.authorDogan, Vedaten
dc.contributor.authorPrestwich, Steven D.en
dc.contributor.editorNicosia, Giuseppeen
dc.contributor.editorOjha, Varunen
dc.contributor.editorLa Malfa, Emanueleen
dc.contributor.editorLa Malfa, Gabrieleen
dc.contributor.editorPardalos, Panos M.en
dc.contributor.editorUmeton, Renatoen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2024-04-09T13:30:01Z
dc.date.available2024-04-09T13:30:01Z
dc.date.issued2024-02-16en
dc.description.abstractBilevel optimization problems have two decision-makers: a leader and a follower (sometimes more than one of either, or both). The leader must solve a constrained optimization problem in which some decisions are made by the follower. These problems are much harder to solve than those with a single decision-maker, and efficient optimal algorithms are known only for special cases. A recent heuristic approach is to treat the leader as an expensive black-box function, to be estimated by Bayesian optimization. We propose a novel approach called ConBaBo to solve bilevel problems, using a new conditional Bayesian optimization algorithm to condition previous decisions in the bilevel decision-making process. This allows it to extract knowledge from earlier decisions by both the leader and follower. We present empirical results showing that this enhances search performance and that ConBaBo outperforms some top-performing algorithms in the literature on two commonly used benchmark datasets.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDogan, V. and Prestwich, S. (2024)'Bilevel optimization by conditional Bayesian optimization', in Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M. and Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, 14505, pp. 243-258. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_19en
dc.identifier.doihttps://doi.org/10.1007/978-3-031-53969-5_19en
dc.identifier.eissn1611-3349en
dc.identifier.endpage258en
dc.identifier.isbn9783031539688en
dc.identifier.isbn9783031539695en
dc.identifier.issn0302-9743en
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage243en
dc.identifier.urihttps://hdl.handle.net/10468/15780
dc.identifier.volume14505en
dc.language.isoenen
dc.publisherSpringer Nature Ltd.en
dc.relation.ispartofInternational Conference on Machine Learning, Optimization, and Data Science (LOD 2023), Grasmere, United Kingdom, 22-26 September 2023en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2024, the Authors, under exclusive license to Springer Nature Switzerland AG. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectBilevel optimizationen
dc.subjectConditional Bayesian optimizationen
dc.subjectStackelberg Gamesen
dc.subjectGaussian processen
dc.titleBilevel optimization by conditional Bayesian optimizationen
dc.typeConference itemen
dc.typeBook chapteren
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