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dc.contributor.authorRivera, G.
dc.contributor.authorCoello, C.A. 
dc.contributor.authorCruz-Reyes, L.
dc.contributor.authorFernandez, E.R.
dc.contributor.authorGomez-Santillan, C.
dc.contributor.authorRangel-Valdez, N.
dc.date.accessioned2022-02-03T15:44:53Z
dc.date.available2022-02-03T15:44:53Z
dc.date.issued2022-03-01
dc.identifier.issn22106502
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1420
dc.description.abstractIn this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multi-objective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the preferences of the Decision Maker (DM) can be modeled through outranking relations. The introduced algorithm (Interval Outranking-based ACO, IO-ACO) is the first ant-colony optimizer that embeds an outranking model to bear vagueness and ill-definition of the DM's preferences. This capacity is the most differentiating feature of IO-ACO because this issue is highly relevant in practice. IO-ACO biases the search towards the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the solutions that better match the DM's preferences. Two widely studied benchmarks were utilized to measure the efficiency of IO-ACO, i.e., the DTLZ and WFG test suites. Accordingly, IO-ACO was compared with four competitive multi-objective optimizers: The Indicator-based Many-Objective ACO, the Multi-objective Evolutionary Algorithm Based on Decomposition, the Reference Vector-Guided Evolutionary Algorithm using Improved Growing Neural Gas, and the Indicator-based Multi-objective Evolutionary Algorithm with Reference Point Adaptation. The numerical results show that IO-ACO approximates the RoI better than leading metaheuristics based on approximating the Pareto frontier alone.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectInterval outrankingen_US
dc.subjectMany-objective optimizationen_US
dc.subjectSwarm intelligenceen_US
dc.subjectVagueness in the DM's preferencesen_US
dc.titlePreference incorporation into many-objective optimization: An Ant colony algorithm based on interval outrankingen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.swevo.2021.101024en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionen_US
dc.journal.titleSwarm and Evolutionary Computationen_US
dc.volume.number69en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España