Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization

Autores
Loor, Fabricio; Leguizamón, M. Guillermo; Mezura-Montes, Efrén
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this work, we present a new algorithm (AJSO) for high-dimensional single objective problems. It is well known that nding high quality solutions is still a challenge for complex problems like those found in the literature as well as in real world concerning Smart Grids scenarios. Our proposal AJSO is an improvement on a state-of-the-art differential Evolution (DE) based algorithm known as SHADE. More speci cally, AJSO implements two novel mutation strategies and also incorporates a mechanism for mantaining and taking good solutions from a special archive when a particular condition during the exploration process is de- tected. To compare the performance of AJSO, the benchmark given in the WCCI/GECCO 2020 is used. This challenge consisted of opti- mization problems represented in two testbeds of Smart Grids problems. In this paper we adopted the guidelines given in the WCCI/GECCO 2020 competition. Experimental results show that AJSO outperforms SHADE in the two studied testbeds.
Workshop: WBDMD – Bases de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Optimization
Smart Grids
Metaheuristics
Differential Evolution
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:10915/114206
Acceder
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network_name_str SEDICI (UNLP)
spelling Smart Grids Challenge: A competitive variant for Single Objective Numerical OptimizationLoor, FabricioLeguizamón, M. GuillermoMezura-Montes, EfrénCiencias InformáticasOptimizationSmart GridsMetaheuristicsDifferential EvolutionIn this work, we present a new algorithm (AJSO) for high-dimensional single objective problems. It is well known that nding high quality solutions is still a challenge for complex problems like those found in the literature as well as in real world concerning Smart Grids scenarios. Our proposal AJSO is an improvement on a state-of-the-art differential Evolution (DE) based algorithm known as SHADE. More speci cally, AJSO implements two novel mutation strategies and also incorporates a mechanism for mantaining and taking good solutions from a special archive when a particular condition during the exploration process is de- tected. To compare the performance of AJSO, the benchmark given in the WCCI/GECCO 2020 is used. This challenge consisted of opti- mization problems represented in two testbeds of Smart Grids problems. In this paper we adopted the guidelines given in the WCCI/GECCO 2020 competition. Experimental results show that AJSO outperforms SHADE in the two studied testbeds.Workshop: WBDMD – Bases de Datos y Minería de DatosRed de Universidades con Carreras en Informática2020-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/114206enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-4417-90-9info:eu-repo/semantics/reference/hdl/10915/113243info: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:UNLP2025-09-29T11:26:39Zoai:sedici.unlp.edu.ar:10915/114206Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:26:39.724SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
title Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
spellingShingle Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
Loor, Fabricio
Ciencias Informáticas
Optimization
Smart Grids
Metaheuristics
Differential Evolution
title_short Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
title_full Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
title_fullStr Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
title_full_unstemmed Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
title_sort Smart Grids Challenge: A competitive variant for Single Objective Numerical Optimization
dc.creator.none.fl_str_mv Loor, Fabricio
Leguizamón, M. Guillermo
Mezura-Montes, Efrén
author Loor, Fabricio
author_facet Loor, Fabricio
Leguizamón, M. Guillermo
Mezura-Montes, Efrén
author_role author
author2 Leguizamón, M. Guillermo
Mezura-Montes, Efrén
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Optimization
Smart Grids
Metaheuristics
Differential Evolution
topic Ciencias Informáticas
Optimization
Smart Grids
Metaheuristics
Differential Evolution
dc.description.none.fl_txt_mv In this work, we present a new algorithm (AJSO) for high-dimensional single objective problems. It is well known that nding high quality solutions is still a challenge for complex problems like those found in the literature as well as in real world concerning Smart Grids scenarios. Our proposal AJSO is an improvement on a state-of-the-art differential Evolution (DE) based algorithm known as SHADE. More speci cally, AJSO implements two novel mutation strategies and also incorporates a mechanism for mantaining and taking good solutions from a special archive when a particular condition during the exploration process is de- tected. To compare the performance of AJSO, the benchmark given in the WCCI/GECCO 2020 is used. This challenge consisted of opti- mization problems represented in two testbeds of Smart Grids problems. In this paper we adopted the guidelines given in the WCCI/GECCO 2020 competition. Experimental results show that AJSO outperforms SHADE in the two studied testbeds.
Workshop: WBDMD – Bases de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
description In this work, we present a new algorithm (AJSO) for high-dimensional single objective problems. It is well known that nding high quality solutions is still a challenge for complex problems like those found in the literature as well as in real world concerning Smart Grids scenarios. Our proposal AJSO is an improvement on a state-of-the-art differential Evolution (DE) based algorithm known as SHADE. More speci cally, AJSO implements two novel mutation strategies and also incorporates a mechanism for mantaining and taking good solutions from a special archive when a particular condition during the exploration process is de- tected. To compare the performance of AJSO, the benchmark given in the WCCI/GECCO 2020 is used. This challenge consisted of opti- mization problems represented in two testbeds of Smart Grids problems. In this paper we adopted the guidelines given in the WCCI/GECCO 2020 competition. Experimental results show that AJSO outperforms SHADE in the two studied testbeds.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
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/10915/114206
url http://sedici.unlp.edu.ar/handle/10915/114206
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-4417-90-9
info:eu-repo/semantics/reference/hdl/10915/113243
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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