A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems

To evaluate the search capabilities of a multiobjective algorithm, the usual approach is to choose a benchmark of known problems, to perform a fixed number of function evaluations, and to apply a set of quality indicators. However, while real problems could have hundreds or even thousands of decisio...

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Autores: Durillo, Juan J., Nebro, Antonio J., Coello Coello, Carlos A., García Nieto, José Manuel, Luna, Francisco, Alba, Enrique
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2010
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/108409
Acceso en línea:https://hdl.handle.net/11441/108409
https://doi.org/10.1109/TEVC.2009.2034647
Access Level:acceso abierto
Palabra clave:Comparative study
Efficiency
Metaheuristics
Multi-objective optimization
Scalability
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spelling A Study of Multiobjective Metaheuristics When Solving Parameter Scalable ProblemsDurillo, Juan J.Nebro, Antonio J.Coello Coello, Carlos A.García Nieto, José ManuelLuna, FranciscoAlba, EnriqueComparative studyEfficiencyMetaheuristicsMulti-objective optimizationScalabilityTo evaluate the search capabilities of a multiobjective algorithm, the usual approach is to choose a benchmark of known problems, to perform a fixed number of function evaluations, and to apply a set of quality indicators. However, while real problems could have hundreds or even thousands of decision variables, current benchmarks are normally adopted with relatively few decision variables (normally from 10 to 30). Furthermore, performing a constant number of evaluations does not provide information about the effort required by an algorithm to get a satisfactory set of solutions; this information would also be of interest in real scenarios, where evaluating the functions defining the problem can be computationally expensive. In this paper, we study the effect of parameter scalability in a number of state-of-the-art multiobjective metaheuristics. We adopt a benchmark of parameter-wise scalable problems (the Zitzler–Deb–Thiele test suite) and analyze the behavior of eight multiobjective metaheuristics on these test problems when using a number of decision variables that range from 8 up to 2048. By using the hypervolume indicator as a stopping condition, we also analyze the computational effort required by each algorithm in order to reach the Pareto front. We conclude that the two analyzed algorithms based on particle swarm optimization and differential evolution yield the best overall results.Junta de Andalucía P07-TIC-03044Ministerio de Ciencia e Innovación TIN2008-06491-C04-01IEEE Computer SocietyCiencias de la Computación e Inteligencia ArtificialJunta de AndalucíaMinisterio de Ciencia e Innovación (MICIN). España2010info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/108409https://doi.org/10.1109/TEVC.2009.2034647reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Evolutionary Computation, 14 (4), 618-635.P07-TIC-03044TIN2008-06491-C04-01https://ieeexplore.ieee.org/document/5415586info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1084092026-06-17T12:51:07Z
dc.title.none.fl_str_mv A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
title A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
spellingShingle A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
Durillo, Juan J.
Comparative study
Efficiency
Metaheuristics
Multi-objective optimization
Scalability
title_short A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
title_full A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
title_fullStr A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
title_full_unstemmed A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
title_sort A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
dc.creator.none.fl_str_mv Durillo, Juan J.
Nebro, Antonio J.
Coello Coello, Carlos A.
García Nieto, José Manuel
Luna, Francisco
Alba, Enrique
author Durillo, Juan J.
author_facet Durillo, Juan J.
Nebro, Antonio J.
Coello Coello, Carlos A.
García Nieto, José Manuel
Luna, Francisco
Alba, Enrique
author_role author
author2 Nebro, Antonio J.
Coello Coello, Carlos A.
García Nieto, José Manuel
Luna, Francisco
Alba, Enrique
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
Junta de Andalucía
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Comparative study
Efficiency
Metaheuristics
Multi-objective optimization
Scalability
topic Comparative study
Efficiency
Metaheuristics
Multi-objective optimization
Scalability
description To evaluate the search capabilities of a multiobjective algorithm, the usual approach is to choose a benchmark of known problems, to perform a fixed number of function evaluations, and to apply a set of quality indicators. However, while real problems could have hundreds or even thousands of decision variables, current benchmarks are normally adopted with relatively few decision variables (normally from 10 to 30). Furthermore, performing a constant number of evaluations does not provide information about the effort required by an algorithm to get a satisfactory set of solutions; this information would also be of interest in real scenarios, where evaluating the functions defining the problem can be computationally expensive. In this paper, we study the effect of parameter scalability in a number of state-of-the-art multiobjective metaheuristics. We adopt a benchmark of parameter-wise scalable problems (the Zitzler–Deb–Thiele test suite) and analyze the behavior of eight multiobjective metaheuristics on these test problems when using a number of decision variables that range from 8 up to 2048. By using the hypervolume indicator as a stopping condition, we also analyze the computational effort required by each algorithm in order to reach the Pareto front. We conclude that the two analyzed algorithms based on particle swarm optimization and differential evolution yield the best overall results.
publishDate 2010
dc.date.none.fl_str_mv 2010
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 https://hdl.handle.net/11441/108409
https://doi.org/10.1109/TEVC.2009.2034647
url https://hdl.handle.net/11441/108409
https://doi.org/10.1109/TEVC.2009.2034647
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Evolutionary Computation, 14 (4), 618-635.
P07-TIC-03044
TIN2008-06491-C04-01
https://ieeexplore.ieee.org/document/5415586
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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