Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm

Autores
Carballido, Jessica Andrea; Ponzoni, Ignacio; Brignole, Nélida B.
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
A Multi-Objective Genetic Algorithm (MOGA) application, which is based on the aggregating approach, is proposed in this article. Its aim is to find a consistent instrument configuration for industrial process plants that will constitute a convenient initial set of input data for structural Observability Analysis Algorithms (OAs). The better this configuration is, the faster the OAs will converge to a satisfactory solution. Algorithmic effectiveness was evaluated through the analysis of small academic case studies. The results obtained through our algorithm show excellent performance. Therefore, it can be stated that the prototype presented in this work is good enough to serve as a sound basis for the development of the definitive MOGA module, whose implementation will support large-size industrial plant models.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Multi-Objective Genetic Algorithm
aggregating approach
industrial process plants
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:10915/184869
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/184869
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Initial Sensor Network Design with a Multi-Objetive Genetic AlgorithmCarballido, Jessica AndreaPonzoni, IgnacioBrignole, Nélida B.Ciencias InformáticasMulti-Objective Genetic Algorithmaggregating approachindustrial process plantsA Multi-Objective Genetic Algorithm (MOGA) application, which is based on the aggregating approach, is proposed in this article. Its aim is to find a consistent instrument configuration for industrial process plants that will constitute a convenient initial set of input data for structural Observability Analysis Algorithms (OAs). The better this configuration is, the faster the OAs will converge to a satisfactory solution. Algorithmic effectiveness was evaluated through the analysis of small academic case studies. The results obtained through our algorithm show excellent performance. Therefore, it can be stated that the prototype presented in this work is good enough to serve as a sound basis for the development of the definitive MOGA module, whose implementation will support large-size industrial plant models.Sociedad Argentina de Informática e Investigación Operativa2003-09info: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/10915/184869enginfo:eu-repo/semantics/altIdentifier/issn/1666-1079info: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:UNLP2025-09-29T11:50:37Zoai:sedici.unlp.edu.ar:10915/184869Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:50:37.445SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
title Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
spellingShingle Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
Carballido, Jessica Andrea
Ciencias Informáticas
Multi-Objective Genetic Algorithm
aggregating approach
industrial process plants
title_short Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
title_full Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
title_fullStr Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
title_full_unstemmed Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
title_sort Initial Sensor Network Design with a Multi-Objetive Genetic Algorithm
dc.creator.none.fl_str_mv Carballido, Jessica Andrea
Ponzoni, Ignacio
Brignole, Nélida B.
author Carballido, Jessica Andrea
author_facet Carballido, Jessica Andrea
Ponzoni, Ignacio
Brignole, Nélida B.
author_role author
author2 Ponzoni, Ignacio
Brignole, Nélida B.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Multi-Objective Genetic Algorithm
aggregating approach
industrial process plants
topic Ciencias Informáticas
Multi-Objective Genetic Algorithm
aggregating approach
industrial process plants
dc.description.none.fl_txt_mv A Multi-Objective Genetic Algorithm (MOGA) application, which is based on the aggregating approach, is proposed in this article. Its aim is to find a consistent instrument configuration for industrial process plants that will constitute a convenient initial set of input data for structural Observability Analysis Algorithms (OAs). The better this configuration is, the faster the OAs will converge to a satisfactory solution. Algorithmic effectiveness was evaluated through the analysis of small academic case studies. The results obtained through our algorithm show excellent performance. Therefore, it can be stated that the prototype presented in this work is good enough to serve as a sound basis for the development of the definitive MOGA module, whose implementation will support large-size industrial plant models.
Sociedad Argentina de Informática e Investigación Operativa
description A Multi-Objective Genetic Algorithm (MOGA) application, which is based on the aggregating approach, is proposed in this article. Its aim is to find a consistent instrument configuration for industrial process plants that will constitute a convenient initial set of input data for structural Observability Analysis Algorithms (OAs). The better this configuration is, the faster the OAs will converge to a satisfactory solution. Algorithmic effectiveness was evaluated through the analysis of small academic case studies. The results obtained through our algorithm show excellent performance. Therefore, it can be stated that the prototype presented in this work is good enough to serve as a sound basis for the development of the definitive MOGA module, whose implementation will support large-size industrial plant models.
publishDate 2003
dc.date.none.fl_str_mv 2003-09
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/10915/184869
url http://sedici.unlp.edu.ar/handle/10915/184869
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1666-1079
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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score 13.070432

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