Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking

In 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....

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Detalles Bibliográficos
Autores: Rivera, G., Coello, C.A., Cruz-Reyes, L., Fernandez, E.R., Gomez-Santillan, C., Rangel-Valdez, N.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1556
Acceso en línea:http://hdl.handle.net/20.500.11824/1556
Access Level:acceso abierto
Palabra clave:Interval outranking
Many-objective optimization
Swarm intelligence
Vagueness in the DM's preferences
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spelling Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking Rivera, G. Coello, C.A. Cruz-Reyes, L. Fernandez, E.R. Gomez-Santillan, C. Rangel-Valdez, N. Interval outranking Many-objective optimization Swarm intelligence Vagueness in the DM's preferences In 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. http://hdl.handle.net/20.500.11824/1556
title Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
spellingShingle Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
Rivera, G.
Interval outranking
Many-objective optimization
Swarm intelligence
Vagueness in the DM's preferences
title_short Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
title_full Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
title_fullStr Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
title_full_unstemmed Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
title_sort Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
author Rivera, G.
author_facet Rivera, G.
Coello, C.A.
Cruz-Reyes, L.
Fernandez, E.R.
Gomez-Santillan, C.
Rangel-Valdez, N.
author_role author
author2 Coello, C.A.
Cruz-Reyes, L.
Fernandez, E.R.
Gomez-Santillan, C.
Rangel-Valdez, N.
author2_role author
author
author
author
author
topic Interval outranking
Many-objective optimization
Swarm intelligence
Vagueness in the DM's preferences
topic_facet Interval outranking
Many-objective optimization
Swarm intelligence
Vagueness in the DM's preferences
description In 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.
publishDate 2022
format article
status_str publishedVersion
url http://hdl.handle.net/20.500.11824/1556
eu_rights_str_mv openAccess
institution Basque Center for Applied Mathematics (BCAM)
collection BIRD. BCAM's Institutional Repository Data
reponame_str BIRD. BCAM's Institutional Repository Data
instname_str Basque Center for Applied Mathematics (BCAM)
_version_ 1878432292134715392
publishDateSort 2022
author_browse Coello, C.A.
Cruz-Reyes, L.
Fernandez, E.R.
Gomez-Santillan, C.
Rangel-Valdez, N.
Rivera, G.
score 6.924472

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