Hierarchical clustering-based framework for a posteriori exploration of pareto fronts : application on the bi-objective next release problem

dc.creatorCasanova Pietroboni, Carlos Antonio
dc.creatorSchab, Esteban Alejandro
dc.creatorPrado, Lucas Martín
dc.creatorRottoli, Giovani Daian
dc.creator.orcid0000-0002-2142-2187
dc.creator.orcid0000-0002-7623-2591
dc.date.accessioned2024-12-27T14:10:44Z
dc.date.issued2023-05-24
dc.description.abstractWhen solving multi-objective combinatorial optimization problems using a search algorithm without a priori information, the result is a Pareto front. Selecting a solution from it is a laborious task if the number of solutions to be analyzed is large. This task would benefit from a systematic approach that facilitates the analysis, comparison and selection of a solution or a group of solutions based on the preferences of the decision makers. In the last decade, the research and development of algorithms for solving multi-objective combinatorial optimization problems has been growing steadily. In contrast, efforts in the a posteriori exploration of non-dominated solutions are still scarce.
dc.description.affiliationFil: Casanova Pietroboni, Carlos Antonio. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay. Departamento Ingeniería en Sistemas de Información. Grupo de Investigación Inteligencia Computacional e Ingeniería de Software; Argentina.
dc.description.affiliationFil: Schab, Esteban Alejandro. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay. Departamento Ingeniería en Sistemas de Información. Grupo de Investigación Inteligencia Computacional e Ingeniería de Software; Argentina.
dc.description.affiliationFil: Prado, Lucas Martín. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay. Departamento Ingeniería en Sistemas de Información. Grupo de Investigación Inteligencia Computacional e Ingeniería de Software; Argentina.
dc.description.affiliationFil: Rottoli, Giovanni Daián. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay. Departamento Ingeniería en Sistemas de Información. Grupo de Investigación Inteligencia Computacional e Ingeniería de Software; Argentina.
dc.formatpdf
dc.identifier.citationFrontiers in computer science
dc.identifier.doihttps://doi.org/10.3389/fcomp.2023.1179059
dc.identifier.urihttp://hdl.handle.net/20.500.12272/12046
dc.language.isoen
dc.publisherHector Florez, Universidad Distrital Francisco Jose de Caldas, Colombia.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.holderCasanova Pietroboni, Carlos Antonio ; Schab, Esteban Alejandro ; Prado, Lucas Martín ; Rottoli, Giovanni Daián.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.useNo comercial con fines académicos. Licencia Creative Commons CC BY.
dc.sourceFrontiers in Computer Science, Sci. 5:1179059, 1-18. (2023)
dc.subjectSearch-based software engineering
dc.subjectPreference-based algorithms
dc.subjectAposteriori approach
dc.subjectHierarchical clustering
dc.subjectMultiobjective optimization
dc.subjectPareto front
dc.titleHierarchical clustering-based framework for a posteriori exploration of pareto fronts : application on the bi-objective next release problem
dc.typeinfo:eu-repo/semantics/article
dc.type.versionpublisherVersion

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