CGD-GA: A graph-based genetic algorithm for sensor network design

Autores
Carballido, Jessica Andrea; Ponzoni, Ignacio; Brignole, Nélida Beatriz
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The foundations and implementation of a genetic algorithm (GA) for instrumentation purposes are presented in this paper. The GA constitutes an initialization module of a decision support system for sensor network design. The method development entailed the definition of the individual's representation as well as the design of a graph-based fitness function, along with the formulation of several other ad hoc implemented features. The performance and effectiveness of the GA were assessed by initializing the instrumentation design of an ammonia synthesis plant. The initialization provided by the GA succeeded in accelerating the sensor network design procedures. It also accomplished a great improvement in the overall quality of the resulting instrument configuration. Therefore, the GA constitutes a valuable tool for the treatment of real industrial problems.
Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Brignole, Nélida Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Materia
Combinatorial Optimization Problem
Genetic Algorithm
Observability Analysis
Process System Engineering
Process-Plant Instrumentation Design
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/83561
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling CGD-GA: A graph-based genetic algorithm for sensor network designCarballido, Jessica AndreaPonzoni, IgnacioBrignole, Nélida BeatrizCombinatorial Optimization ProblemGenetic AlgorithmObservability AnalysisProcess System EngineeringProcess-Plant Instrumentation Designhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The foundations and implementation of a genetic algorithm (GA) for instrumentation purposes are presented in this paper. The GA constitutes an initialization module of a decision support system for sensor network design. The method development entailed the definition of the individual's representation as well as the design of a graph-based fitness function, along with the formulation of several other ad hoc implemented features. The performance and effectiveness of the GA were assessed by initializing the instrumentation design of an ammonia synthesis plant. The initialization provided by the GA succeeded in accelerating the sensor network design procedures. It also accomplished a great improvement in the overall quality of the resulting instrument configuration. Therefore, the GA constitutes a valuable tool for the treatment of real industrial problems.Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Brignole, Nélida Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaElsevier Science Inc2007-11-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/83561Carballido, Jessica Andrea; Ponzoni, Ignacio; Brignole, Nélida Beatriz; CGD-GA: A graph-based genetic algorithm for sensor network design; Elsevier Science Inc; Information Sciences; 177; 22; 2-11-2007; 5091-51020020-0255CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S002002550700271Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2007.05.036info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:14:54Zoai:ri.conicet.gov.ar:11336/83561instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:14:54.613CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv CGD-GA: A graph-based genetic algorithm for sensor network design
title CGD-GA: A graph-based genetic algorithm for sensor network design
spellingShingle CGD-GA: A graph-based genetic algorithm for sensor network design
Carballido, Jessica Andrea
Combinatorial Optimization Problem
Genetic Algorithm
Observability Analysis
Process System Engineering
Process-Plant Instrumentation Design
title_short CGD-GA: A graph-based genetic algorithm for sensor network design
title_full CGD-GA: A graph-based genetic algorithm for sensor network design
title_fullStr CGD-GA: A graph-based genetic algorithm for sensor network design
title_full_unstemmed CGD-GA: A graph-based genetic algorithm for sensor network design
title_sort CGD-GA: A graph-based genetic algorithm for sensor network design
dc.creator.none.fl_str_mv Carballido, Jessica Andrea
Ponzoni, Ignacio
Brignole, Nélida Beatriz
author Carballido, Jessica Andrea
author_facet Carballido, Jessica Andrea
Ponzoni, Ignacio
Brignole, Nélida Beatriz
author_role author
author2 Ponzoni, Ignacio
Brignole, Nélida Beatriz
author2_role author
author
dc.subject.none.fl_str_mv Combinatorial Optimization Problem
Genetic Algorithm
Observability Analysis
Process System Engineering
Process-Plant Instrumentation Design
topic Combinatorial Optimization Problem
Genetic Algorithm
Observability Analysis
Process System Engineering
Process-Plant Instrumentation Design
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The foundations and implementation of a genetic algorithm (GA) for instrumentation purposes are presented in this paper. The GA constitutes an initialization module of a decision support system for sensor network design. The method development entailed the definition of the individual's representation as well as the design of a graph-based fitness function, along with the formulation of several other ad hoc implemented features. The performance and effectiveness of the GA were assessed by initializing the instrumentation design of an ammonia synthesis plant. The initialization provided by the GA succeeded in accelerating the sensor network design procedures. It also accomplished a great improvement in the overall quality of the resulting instrument configuration. Therefore, the GA constitutes a valuable tool for the treatment of real industrial problems.
Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Brignole, Nélida Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
description The foundations and implementation of a genetic algorithm (GA) for instrumentation purposes are presented in this paper. The GA constitutes an initialization module of a decision support system for sensor network design. The method development entailed the definition of the individual's representation as well as the design of a graph-based fitness function, along with the formulation of several other ad hoc implemented features. The performance and effectiveness of the GA were assessed by initializing the instrumentation design of an ammonia synthesis plant. The initialization provided by the GA succeeded in accelerating the sensor network design procedures. It also accomplished a great improvement in the overall quality of the resulting instrument configuration. Therefore, the GA constitutes a valuable tool for the treatment of real industrial problems.
publishDate 2007
dc.date.none.fl_str_mv 2007-11-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/83561
Carballido, Jessica Andrea; Ponzoni, Ignacio; Brignole, Nélida Beatriz; CGD-GA: A graph-based genetic algorithm for sensor network design; Elsevier Science Inc; Information Sciences; 177; 22; 2-11-2007; 5091-5102
0020-0255
CONICET Digital
CONICET
url http://hdl.handle.net/11336/83561
identifier_str_mv Carballido, Jessica Andrea; Ponzoni, Ignacio; Brignole, Nélida Beatriz; CGD-GA: A graph-based genetic algorithm for sensor network design; Elsevier Science Inc; Information Sciences; 177; 22; 2-11-2007; 5091-5102
0020-0255
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S002002550700271X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2007.05.036
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Inc
publisher.none.fl_str_mv Elsevier Science Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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