Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms

Autores
Bilbao, Martín; Leguizamón, Guillermo
Año de publicación
2019
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.
XX Workshop Agentes y Sistemas Inteligentes.
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Wind Energy
Weibull Distribution
Wind Power
Evolutionary Computation
Metaheuristics
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:10915/90896
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind FarmsBilbao, MartínLeguizamón, GuillermoCiencias InformáticasWind EnergyWeibull DistributionWind PowerEvolutionary ComputationMetaheuristicsIn this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.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/pdf85-94http://sedici.unlp.edu.ar/handle/10915/90896spainfo: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:UNLP2025-10-22T16:59:53Zoai:sedici.unlp.edu.ar:10915/90896Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:59:53.285SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
title Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
spellingShingle Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
Bilbao, Martín
Ciencias Informáticas
Wind Energy
Weibull Distribution
Wind Power
Evolutionary Computation
Metaheuristics
title_short Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
title_full Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
title_fullStr Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
title_full_unstemmed Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
title_sort Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms
dc.creator.none.fl_str_mv Bilbao, Martín
Leguizamón, Guillermo
author Bilbao, Martín
author_facet Bilbao, Martín
Leguizamón, Guillermo
author_role author
author2 Leguizamón, Guillermo
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Wind Energy
Weibull Distribution
Wind Power
Evolutionary Computation
Metaheuristics
topic Ciencias Informáticas
Wind Energy
Weibull Distribution
Wind Power
Evolutionary Computation
Metaheuristics
dc.description.none.fl_txt_mv In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.
XX Workshop Agentes y Sistemas Inteligentes.
Red de Universidades con Carreras en Informática
description In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.
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/10915/90896
url http://sedici.unlp.edu.ar/handle/10915/90896
dc.language.none.fl_str_mv spa
language spa
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
85-94
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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