A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem

dc.creatorRiveros, Francisco
dc.creatorBenítez, Néstor
dc.creatorPaciello, Julio
dc.creatorBarán, Benjamín
dc.date2016-11
dc.date2016-12-05T12:28:51Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/57269
dc.identifierhttp://journal.info.unlp.edu.ar/wp-content/uploads/2016/12/JCST-43-Paper-4.pdf
dc.identifierissn:1666-6038
dc.descriptionEvolutionary algorithms present performance drawbacks when applied to Many-objective Optimization Problems (MaOPs). In this work, a novel approach based on Ant Colony Optimization theory (ACO), denominated ACO λ base-p algorithm, is proposed in order to handle Manyobjective instances of the well-known Traveling Salesman Problem (TSP). The proposed algorithm was applied to several Many-objective TSP instances, verifying the quality of the experimental results using the Hypervolume metric. A comparison with other state-of-the-art Multi Objective ACO algorithms as MAS, M3AS and MOACS as well as NSGA2 evolutionary algorithm was made, verifying that the best experimental results were obtained when the proposed algorithm was used, proving a good applicability to MaOPs.
dc.descriptionFacultad de Informática
dc.formatapplication/pdf
dc.format89-94
dc.languageen
dc.relationJournal of Computer Science & Technology
dc.relationvol. 16, no. 2
dc.rightshttp://creativecommons.org/licenses/by/3.0/
dc.rightsCreative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.subjectCiencias Informáticas
dc.titleA Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
dc.typeArticulo
dc.typeArticulo


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