Initial Sensor Network Design with a Multi-Objective Genetic Algorithm
- Autores
- Carballido, Jessica Andrea; Ponzoni, Ignacio; Brignole, Nélida B.
- Año de publicación
- 2004
- Idioma
- inglés
- Tipo de recurso
- artículo
- 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 Optimization
Genetic algorithms
Sensor Network Design - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/135315
Ver los metadatos del registro completo
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Initial Sensor Network Design with a Multi-Objective Genetic AlgorithmCarballido, Jessica AndreaPonzoni, IgnacioBrignole, Nélida B.Ciencias InformáticasMulti-Objective OptimizationGenetic algorithmsSensor Network DesignA 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 Operativa2004-06-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf34-41http://sedici.unlp.edu.ar/handle/10915/135315enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/106info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:06:08Zoai:sedici.unlp.edu.ar:10915/135315Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:06:08.806SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Initial Sensor Network Design with a Multi-Objective Genetic Algorithm |
title |
Initial Sensor Network Design with a Multi-Objective Genetic Algorithm |
spellingShingle |
Initial Sensor Network Design with a Multi-Objective Genetic Algorithm Carballido, Jessica Andrea Ciencias Informáticas Multi-Objective Optimization Genetic algorithms Sensor Network Design |
title_short |
Initial Sensor Network Design with a Multi-Objective Genetic Algorithm |
title_full |
Initial Sensor Network Design with a Multi-Objective Genetic Algorithm |
title_fullStr |
Initial Sensor Network Design with a Multi-Objective Genetic Algorithm |
title_full_unstemmed |
Initial Sensor Network Design with a Multi-Objective Genetic Algorithm |
title_sort |
Initial Sensor Network Design with a Multi-Objective 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 Optimization Genetic algorithms Sensor Network Design |
topic |
Ciencias Informáticas Multi-Objective Optimization Genetic algorithms Sensor Network Design |
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 |
2004 |
dc.date.none.fl_str_mv |
2004-06-26 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/135315 |
url |
http://sedici.unlp.edu.ar/handle/10915/135315 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/106 info:eu-repo/semantics/altIdentifier/issn/1514-6774 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
dc.format.none.fl_str_mv |
application/pdf 34-41 |
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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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SEDICI (UNLP) |
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SEDICI (UNLP) |
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Universidad Nacional de La Plata |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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score |
13.13397 |