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A trust-region approach for computing Pareto fronts in multiobjective optimization

2024-01Artigo científico Acesso aberto
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dc.contributor.authorMohammadi, A.
dc.contributor.authorCustódio, A. L.
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.institutionDM - Departamento de Matemática
dc.contributor.pblSpringer Science Business Media
dc.date.accessioned2024-09-17T22:21:27Z
dc.date.available2024-09-17T22:21:27Z
dc.date.issued2024-01
dc.descriptionFunding Information: This work was funded by national funds through FCT - Fundação para a Ciência e a Tecnologia I.P., under the scope of projects PTDC/MAT-APL/28400/2017, UIDP/00297/2020, and UIDB/00297/2020 (Center for Mathematics and Applications). The work of the first author was additionally supported by the scholarship 2020.08249.BD, also granted by FCT - Fundação para a Ciência e a Tecnologia I.P.. Publisher Copyright: © 2023, The Author(s).
dc.description.abstractMultiobjective optimization is a challenging scientific area, where the conflicting nature of the different objectives to be optimized changes the concept of problem solution, which is no longer a single point but a set of points, namely the Pareto front. In a posteriori preferences approach, when the decision maker is unable to rank objectives before the optimization, it is important to develop algorithms that generate approximations to the complete Pareto front of a multiobjective optimization problem, making clear the trade-offs between the different objectives. In this work, an algorithm based on a trust-region approach is proposed to approximate the set of Pareto critical points of a multiobjective optimization problem. Derivatives are assumed to be known, allowing the computation of Taylor models for the different objective function components, which will be minimized in two main steps: the extreme point step and the scalarization step. The goal of the extreme point step is to expand the approximation to the Pareto front, by moving towards the extreme points of it, corresponding to the individual minimization of each objective function component. The scalarization step attempts to reduce the gaps on the Pareto front, by solving adequate scalarization problems. The convergence of the method is analyzed and numerical experiments are reported, indicating the relevance of each feature included in the algorithmic structure and its competitiveness, by comparison against a state-of-art multiobjective optimization algorithm.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent31
dc.format.extent1511175
dc.identifier.doi10.1007/s10589-023-00510-2
dc.identifier.issn0926-6003
dc.identifier.otherPURE: 99126420
dc.identifier.otherPURE UUID: b66bc683-a6d8-47fd-82a9-85a33e3f10bb
dc.identifier.otherScopus: 85168362172
dc.identifier.otherWOS: 001051147000001
dc.identifier.urihttp://hdl.handle.net/10362/171956
dc.identifier.urlhttps://www.scopus.com/pages/publications/85168362172
dc.language.isoeng
dc.peerreviewedyes
dc.subjectMultiobjective optimization
dc.subjectPareto front
dc.subjectScalarization techniques
dc.subjectTaylor models
dc.subjectTrust-region methods
dc.subjectControl and Optimization
dc.subjectComputational Mathematics
dc.subjectApplied Mathematics
dc.titleA trust-region approach for computing Pareto fronts in multiobjective optimizationen
dc.typejournal article
degois.publication.firstPage149
degois.publication.issue1
degois.publication.lastPage179
degois.publication.titleComputational Optimization And Applications
degois.publication.volume87
dspace.entity.typePublication
rcaap.rightsopenAccess

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