Outer approximation algorithms for convex vector optimization problems

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buir.contributor.authorKeskin, İrem Nur
buir.contributor.authorUlus, Firdevs
buir.contributor.orcidKeskin, İrem Nur|0000-0001-9530-7889
buir.contributor.orcidUlus, Firdevs|0000-0002-0532-9927
dc.citation.epage755en_US
dc.citation.issueNumber4
dc.citation.spage723
dc.citation.volumeNumber38
dc.contributor.authorKeskin, İrem Nur
dc.contributor.authorUlus, Firdevs
dc.date.accessioned2024-03-21T19:01:21Z
dc.date.available2024-03-21T19:01:21Z
dc.date.issued2023-02-09
dc.departmentDepartment of Industrial Engineering
dc.description.abstractIn this study, we present a general framework of outer approximation algorithms to solve convex vector optimization problems, in which the Pascoletti-Serafini (PS) scalarization is solved iteratively. This scalarization finds the minimum ‘distance’ from a reference point, which is usually taken as a vertex of the current outer approximation, to the upper image through a given direction. We propose efficient methods to select the parameters (the reference point and direction vector) of the PS scalarization and analyse the effects of these on the overall performance of the algorithm. Different from the existing vertex selection rules from the literature, the proposed methods do not require solving additional single-objective optimization problems. Using some test problems, we conduct an extensive computational study where three different measures are set as the stopping criteria: the approximation error, the runtime, and the cardinality of the solution set. We observe that the proposed variants have satisfactory results, especially in terms of runtime compared to the existing variants from the literature. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
dc.identifier.doi10.1080/10556788.2023.2167994
dc.identifier.eissn1029-4937
dc.identifier.issn1055-6788
dc.identifier.urihttps://hdl.handle.net/11693/115065
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.isversionofhttps://dx.doi.org/10.1080/10556788.2023.2167994
dc.rightsCC BY-NC-ND 4.0 DEED (Attribution-NonCommercial-NoDerivs 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source.titleOptimization Methods and Software
dc.subjectMultiobjective optimization
dc.subjectConvex vector optimization
dc.subjectApproximation algorithms
dc.subjectPascoletti–Serafini scalarization
dc.titleOuter approximation algorithms for convex vector optimization problems
dc.typeArticle

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