An evolutionary approach for the design of nonredundant sensor networks

Autores
Carnero, Mercedes del Carmen; Hernández, José; Sanchez, Mabel Cristina; Bandoni, Jose Alberto
Año de publicación
2001
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, solution strategies for the optimal design of nonredundant observable linear sensor networks are discussed. The Greedy algorithm allows the problem only to be tackled for a subset of optimization criteria. Particular deterministic techniques or general evolutionary strategies are necessary to solve the problem for more complex objective functions. In this context, a procedure based on the application of genetic algorithms (GAs) and linear algebra is presented. Ad hoc operators are designed for the crossover and mutation operations because the classic genetic operators perform poorly. In contrast to ad hoc deterministic codes, which find the design solution for each specific criteria, this strategy allows the problem to be solved with different objective functions using the same implementation. Furthermore, this code is extended to handle multiobjective problems through a modification of only the selection operator. An industrial example is provided to show the efficiency of the algorithm.
Fil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Hernández, José. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Sanchez, Mabel Cristina. 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
Fil: Bandoni, Jose Alberto. 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
Materia
Sensor Networks
Genetic Algorithms
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:11336/38063
Acceder
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network_name_str CONICET Digital (CONICET)
spelling An evolutionary approach for the design of nonredundant sensor networksCarnero, Mercedes del CarmenHernández, JoséSanchez, Mabel CristinaBandoni, Jose AlbertoSensor NetworksGenetic Algorithmshttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In this work, solution strategies for the optimal design of nonredundant observable linear sensor networks are discussed. The Greedy algorithm allows the problem only to be tackled for a subset of optimization criteria. Particular deterministic techniques or general evolutionary strategies are necessary to solve the problem for more complex objective functions. In this context, a procedure based on the application of genetic algorithms (GAs) and linear algebra is presented. Ad hoc operators are designed for the crossover and mutation operations because the classic genetic operators perform poorly. In contrast to ad hoc deterministic codes, which find the design solution for each specific criteria, this strategy allows the problem to be solved with different objective functions using the same implementation. Furthermore, this code is extended to handle multiobjective problems through a modification of only the selection operator. An industrial example is provided to show the efficiency of the algorithm.Fil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Hernández, José. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Sanchez, Mabel Cristina. 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; ArgentinaFil: Bandoni, Jose Alberto. 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; ArgentinaAmerican Chemical Society2001-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/38063Carnero, Mercedes del Carmen; Hernández, José; Sanchez, Mabel Cristina; Bandoni, Jose Alberto; An evolutionary approach for the design of nonredundant sensor networks; American Chemical Society; Industrial & Engineering Chemical Research; 40; 23; 11-2001; 5578-55840888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie000941kinfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/abs/10.1021/ie000941kinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:03:51Zoai:ri.conicet.gov.ar:11336/38063instacron: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:34982025-09-10 13:03:51.262CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An evolutionary approach for the design of nonredundant sensor networks
title An evolutionary approach for the design of nonredundant sensor networks
spellingShingle An evolutionary approach for the design of nonredundant sensor networks
Carnero, Mercedes del Carmen
Sensor Networks
Genetic Algorithms
title_short An evolutionary approach for the design of nonredundant sensor networks
title_full An evolutionary approach for the design of nonredundant sensor networks
title_fullStr An evolutionary approach for the design of nonredundant sensor networks
title_full_unstemmed An evolutionary approach for the design of nonredundant sensor networks
title_sort An evolutionary approach for the design of nonredundant sensor networks
dc.creator.none.fl_str_mv Carnero, Mercedes del Carmen
Hernández, José
Sanchez, Mabel Cristina
Bandoni, Jose Alberto
author Carnero, Mercedes del Carmen
author_facet Carnero, Mercedes del Carmen
Hernández, José
Sanchez, Mabel Cristina
Bandoni, Jose Alberto
author_role author
author2 Hernández, José
Sanchez, Mabel Cristina
Bandoni, Jose Alberto
author2_role author
author
author
dc.subject.none.fl_str_mv Sensor Networks
Genetic Algorithms
topic Sensor Networks
Genetic Algorithms
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work, solution strategies for the optimal design of nonredundant observable linear sensor networks are discussed. The Greedy algorithm allows the problem only to be tackled for a subset of optimization criteria. Particular deterministic techniques or general evolutionary strategies are necessary to solve the problem for more complex objective functions. In this context, a procedure based on the application of genetic algorithms (GAs) and linear algebra is presented. Ad hoc operators are designed for the crossover and mutation operations because the classic genetic operators perform poorly. In contrast to ad hoc deterministic codes, which find the design solution for each specific criteria, this strategy allows the problem to be solved with different objective functions using the same implementation. Furthermore, this code is extended to handle multiobjective problems through a modification of only the selection operator. An industrial example is provided to show the efficiency of the algorithm.
Fil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Hernández, José. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; Argentina
Fil: Sanchez, Mabel Cristina. 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
Fil: Bandoni, Jose Alberto. 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
description In this work, solution strategies for the optimal design of nonredundant observable linear sensor networks are discussed. The Greedy algorithm allows the problem only to be tackled for a subset of optimization criteria. Particular deterministic techniques or general evolutionary strategies are necessary to solve the problem for more complex objective functions. In this context, a procedure based on the application of genetic algorithms (GAs) and linear algebra is presented. Ad hoc operators are designed for the crossover and mutation operations because the classic genetic operators perform poorly. In contrast to ad hoc deterministic codes, which find the design solution for each specific criteria, this strategy allows the problem to be solved with different objective functions using the same implementation. Furthermore, this code is extended to handle multiobjective problems through a modification of only the selection operator. An industrial example is provided to show the efficiency of the algorithm.
publishDate 2001
dc.date.none.fl_str_mv 2001-11
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/11336/38063
Carnero, Mercedes del Carmen; Hernández, José; Sanchez, Mabel Cristina; Bandoni, Jose Alberto; An evolutionary approach for the design of nonredundant sensor networks; American Chemical Society; Industrial & Engineering Chemical Research; 40; 23; 11-2001; 5578-5584
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/38063
identifier_str_mv Carnero, Mercedes del Carmen; Hernández, José; Sanchez, Mabel Cristina; Bandoni, Jose Alberto; An evolutionary approach for the design of nonredundant sensor networks; American Chemical Society; Industrial & Engineering Chemical Research; 40; 23; 11-2001; 5578-5584
0888-5885
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1021/ie000941k
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/abs/10.1021/ie000941k
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
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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 12.993085

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