DE with Random Vector based Mutatiton for High Dimensional Problems

Autores
Hernández, Sebastián; Mezura Montes, Efrén; Leguizamón, Guillermo
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Metaheuristic techniques are the current standard for solving optimization problems. Differential Evolution (DE) is one of the most used because all operations are on real floating point numbers and does not require extra coding. However, the performance shown by DE could decay when applied in problems of high dimensionality. In this paper we present RLSDE, a modified version of DE, based on a random vector as a scaling factor for the differential mutation and the application of a local search operator. These modifications constitute an algorithm capable of solving 100D problems using few computational resources. RLSDE is compared against the results obtained with the classic version of DE and ELSDE (Enchanced Local Search Differential Evolution), showing the performance of the proposal.
XX Workshop Agentes y Sistemas Inteligentes.
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Differential evolution
High-dimensional optimization problem
Local search
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/90893
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/90893
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling DE with Random Vector based Mutatiton for High Dimensional ProblemsHernández, SebastiánMezura Montes, EfrénLeguizamón, GuillermoCiencias InformáticasDifferential evolutionHigh-dimensional optimization problemLocal searchMetaheuristic techniques are the current standard for solving optimization problems. Differential Evolution (DE) is one of the most used because all operations are on real floating point numbers and does not require extra coding. However, the performance shown by DE could decay when applied in problems of high dimensionality. In this paper we present RLSDE, a modified version of DE, based on a random vector as a scaling factor for the differential mutation and the application of a local search operator. These modifications constitute an algorithm capable of solving 100D problems using few computational resources. RLSDE is compared against the results obtained with the classic version of DE and ELSDE (Enchanced Local Search Differential Evolution), showing the performance of the proposal.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informática2019-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf52-63http://sedici.unlp.edu.ar/handle/10915/90893enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1info:eu-repo/semantics/reference/hdl/10915/90359info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:19:03Zoai:sedici.unlp.edu.ar:10915/90893Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:19:03.318SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv DE with Random Vector based Mutatiton for High Dimensional Problems
title DE with Random Vector based Mutatiton for High Dimensional Problems
spellingShingle DE with Random Vector based Mutatiton for High Dimensional Problems
Hernández, Sebastián
Ciencias Informáticas
Differential evolution
High-dimensional optimization problem
Local search
title_short DE with Random Vector based Mutatiton for High Dimensional Problems
title_full DE with Random Vector based Mutatiton for High Dimensional Problems
title_fullStr DE with Random Vector based Mutatiton for High Dimensional Problems
title_full_unstemmed DE with Random Vector based Mutatiton for High Dimensional Problems
title_sort DE with Random Vector based Mutatiton for High Dimensional Problems
dc.creator.none.fl_str_mv Hernández, Sebastián
Mezura Montes, Efrén
Leguizamón, Guillermo
author Hernández, Sebastián
author_facet Hernández, Sebastián
Mezura Montes, Efrén
Leguizamón, Guillermo
author_role author
author2 Mezura Montes, Efrén
Leguizamón, Guillermo
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Differential evolution
High-dimensional optimization problem
Local search
topic Ciencias Informáticas
Differential evolution
High-dimensional optimization problem
Local search
dc.description.none.fl_txt_mv Metaheuristic techniques are the current standard for solving optimization problems. Differential Evolution (DE) is one of the most used because all operations are on real floating point numbers and does not require extra coding. However, the performance shown by DE could decay when applied in problems of high dimensionality. In this paper we present RLSDE, a modified version of DE, based on a random vector as a scaling factor for the differential mutation and the application of a local search operator. These modifications constitute an algorithm capable of solving 100D problems using few computational resources. RLSDE is compared against the results obtained with the classic version of DE and ELSDE (Enchanced Local Search Differential Evolution), showing the performance of the proposal.
XX Workshop Agentes y Sistemas Inteligentes.
Red de Universidades con Carreras en Informática
description Metaheuristic techniques are the current standard for solving optimization problems. Differential Evolution (DE) is one of the most used because all operations are on real floating point numbers and does not require extra coding. However, the performance shown by DE could decay when applied in problems of high dimensionality. In this paper we present RLSDE, a modified version of DE, based on a random vector as a scaling factor for the differential mutation and the application of a local search operator. These modifications constitute an algorithm capable of solving 100D problems using few computational resources. RLSDE is compared against the results obtained with the classic version of DE and ELSDE (Enchanced Local Search Differential Evolution), showing the performance of the proposal.
publishDate 2019
dc.date.none.fl_str_mv 2019-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/90893
url http://sedici.unlp.edu.ar/handle/10915/90893
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1
info:eu-repo/semantics/reference/hdl/10915/90359
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
52-63
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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