Sliding Window 3-Objective Pareto Optimization for Problems with Chance Constraints

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dc.contributor.authorNeumann, F.
dc.contributor.authorWitt, C.
dc.contributor.conferenceInternational Conference on Parallel Problem Solving from Nature (PPSN) (14 Sep 2024 - 18 Sep 2024 : Hagenberg, Austria)
dc.contributor.editorAffenzeller, M.
dc.contributor.editorWinkler, S.M.
dc.contributor.editorKononova, A.V.
dc.contributor.editorTrautmann, H.
dc.contributor.editorTusar, T.
dc.contributor.editorMachado, P.
dc.contributor.editorBack, T.
dc.date.issued2024
dc.description.abstractConstrained single-objective problems have been frequently tackled by evolutionary multi-objective algorithms where the constraint is relaxed into an additional objective. Recently, it has been shown that Pareto optimization approaches using bi-objective models can be significantly sped up using sliding windows [16]. In this paper, we extend the sliding window approach to 3-objective formulations for tackling chance constrained problems. On the theoretical side, we show that our new sliding window approach improves previous runtime bounds obtained in [15] while maintaining the same approximation guarantees. Our experimental investigations for the chance constrained dominating set problem show that our new sliding window approach allows one to solve much larger instances in a much more efficient way than the 3-objective approach presented in [15].
dc.description.statementofresponsibilityFrank Neumann and Carsten Witt
dc.identifier.citationLecture Notes in Artificial Intelligence, 2024 / Affenzeller, M., Winkler, S.M., Kononova, A.V., Trautmann, H., Tusar, T., Machado, P., Back, T. (ed./s), vol.15150, pp.36-52
dc.identifier.doi10.1007/978-3-031-70071-2_3
dc.identifier.isbn978-3-031-70071-2
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttps://hdl.handle.net/2440/145751
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.granthttp://purl.org/au-research/grants/arc/FT200100536
dc.relation.ispartofseriesLecture Notes in Computer Science; 15150
dc.rights© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
dc.source.urihttps://link.springer.com/book/10.1007/978-3-031-70071-2
dc.subjectchance constraints; evolutionary algorithms; multi-objective optimization
dc.titleSliding Window 3-Objective Pareto Optimization for Problems with Chance Constraints
dc.typeConference paper
pubs.publication-statusPublished

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