COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems

Many real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision variable space. Evolutionary Algorithms (EAs) are capable of tackling Multi-o...

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Detalles Bibliográficos
Autores: Llano García, J.L., Monroy, R., Sosa Hernández, V.A., Coello, C.A.
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2021
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1406
Acceso en línea:http://hdl.handle.net/20.500.11824/1406
Access Level:acceso abierto
Palabra clave:Constrained optimization
Evolutionary algorithms
Multi-Objective optimization
Performance indicators
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spelling COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problemsLlano García, J.L.Monroy, R.Sosa Hernández, V.A.Coello, C.A.Constrained optimizationEvolutionary algorithmsMulti-Objective optimizationPerformance indicatorsMany real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision variable space. Evolutionary Algorithms (EAs) are capable of tackling Multi-objective Optimization Problems (MOPs). However, these approaches struggle to accurately approximate a feasible solution when considering equality constraints as part of the problem due to the inability of EAs to find and keep solutions exactly at the constraint boundaries. Here, we present an indicator-based evolutionary multi-objective optimization algorithm (EMOA) for tackling Equality Constrained MOPs (ECMOPs). In our proposal, we adopt an artificially constructed reference set closely resembling the feasible Pareto front of an ECMOP to calculate the Inverted Generational Distance of a population, which is then used as a density estimator. An empirical study over a set of benchmark problems each of which contains at least one equality constraint was performed to test the capabilities of our proposed COnstrAined Reference SEt - EMOA (COARSE-EMOA). Our results are compared to those obtained by six other EMOAs. As will be shown, our proposed COARSE-EMOA can properly approximate a feasible solution by guiding the search through the use of an artificially constructed set that approximates the feasible Pareto front of a given problem.202220222021info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1406reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)InglésReconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/14062026-06-19T12:47:47Z
dc.title.none.fl_str_mv COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
title COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
spellingShingle COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
Llano García, J.L.
Constrained optimization
Evolutionary algorithms
Multi-Objective optimization
Performance indicators
title_short COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
title_full COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
title_fullStr COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
title_full_unstemmed COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
title_sort COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
dc.creator.none.fl_str_mv Llano García, J.L.
Monroy, R.
Sosa Hernández, V.A.
Coello, C.A.
author Llano García, J.L.
author_facet Llano García, J.L.
Monroy, R.
Sosa Hernández, V.A.
Coello, C.A.
author_role author
author2 Monroy, R.
Sosa Hernández, V.A.
Coello, C.A.
author2_role author
author
author
dc.subject.none.fl_str_mv Constrained optimization
Evolutionary algorithms
Multi-Objective optimization
Performance indicators
topic Constrained optimization
Evolutionary algorithms
Multi-Objective optimization
Performance indicators
description Many real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision variable space. Evolutionary Algorithms (EAs) are capable of tackling Multi-objective Optimization Problems (MOPs). However, these approaches struggle to accurately approximate a feasible solution when considering equality constraints as part of the problem due to the inability of EAs to find and keep solutions exactly at the constraint boundaries. Here, we present an indicator-based evolutionary multi-objective optimization algorithm (EMOA) for tackling Equality Constrained MOPs (ECMOPs). In our proposal, we adopt an artificially constructed reference set closely resembling the feasible Pareto front of an ECMOP to calculate the Inverted Generational Distance of a population, which is then used as a density estimator. An empirical study over a set of benchmark problems each of which contains at least one equality constraint was performed to test the capabilities of our proposed COnstrAined Reference SEt - EMOA (COARSE-EMOA). Our results are compared to those obtained by six other EMOAs. As will be shown, our proposed COARSE-EMOA can properly approximate a feasible solution by guiding the search through the use of an artificially constructed set that approximates the feasible Pareto front of a given problem.
publishDate 2021
dc.date.none.fl_str_mv 2021
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1406
url http://hdl.handle.net/20.500.11824/1406
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
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collection BIRD. BCAM's Institutional Repository Data
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