Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis

Autores
Costa, Federico; Quintero Marquina, Gustavo Javier; Riddick, Maximiliano Luis; Andrini, Leandro Ruben; Vahnovan, Alejandra Valeria; Huck Iriart, Cristián
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A Self-Cooling Simulated Annealing (SCSA) algorithm is introduced for the optimization of nonlinear least-squares problems. In contrast to conventional simulated annealing techniques that require a predefined cooling schedule, the SCSA algorithm autonomously regulates system temperature based on the lowest figure of merit (ie. χ²) achieved at each iteration. The algorithm incorporates two key enhancements to improve efficiency: the separation of linear and nonlinear parameters, which reduces the dimensionality of the stochastic search space, and an adaptive Gaussian sampling mechanism that dynamically updates parameter-specific variances based on recent optimization history. A thermal resistance parameter (K) regulates the cooling rate and can be adjusted according to problem complexity. Performance benchmarking against standard Monte Carlo and gradient-based methods demonstrates that SCSA offers greater robustness in avoiding local minima and provides reliable convergence across varying levels of optimization difficulty. These characteristics make the method broadly applicable to nonlinear data analysis and other complex optimization tasks.
Fil: Costa, Federico. Universidad Politécnica de Catalunya; España
Fil: Quintero Marquina, Gustavo Javier. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Tecnologias Emergentes y Ciencias Aplicadas. - Universidad Nacional de San Martin. Instituto de Tecnologias Emergentes y Ciencias Aplicadas.; Argentina
Fil: Riddick, Maximiliano Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Núcleo Consolidado de Matemática Pura y Aplicada; Argentina
Fil: Andrini, Leandro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; Argentina
Fil: Vahnovan, Alejandra Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; Argentina
Fil: Huck Iriart, Cristián. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Tecnologias Emergentes y Ciencias Aplicadas. - Universidad Nacional de San Martin. Instituto de Tecnologias Emergentes y Ciencias Aplicadas.; Argentina
Materia
MONTE CARLO
DATA ANALYSIS
PARAMETRIC FUNCTIONS
SIMULATED ANNEALING
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:11336/278863
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data AnalysisCosta, FedericoQuintero Marquina, Gustavo JavierRiddick, Maximiliano LuisAndrini, Leandro RubenVahnovan, Alejandra ValeriaHuck Iriart, CristiánMONTE CARLODATA ANALYSISPARAMETRIC FUNCTIONSSIMULATED ANNEALINGhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1A Self-Cooling Simulated Annealing (SCSA) algorithm is introduced for the optimization of nonlinear least-squares problems. In contrast to conventional simulated annealing techniques that require a predefined cooling schedule, the SCSA algorithm autonomously regulates system temperature based on the lowest figure of merit (ie. χ²) achieved at each iteration. The algorithm incorporates two key enhancements to improve efficiency: the separation of linear and nonlinear parameters, which reduces the dimensionality of the stochastic search space, and an adaptive Gaussian sampling mechanism that dynamically updates parameter-specific variances based on recent optimization history. A thermal resistance parameter (K) regulates the cooling rate and can be adjusted according to problem complexity. Performance benchmarking against standard Monte Carlo and gradient-based methods demonstrates that SCSA offers greater robustness in avoiding local minima and provides reliable convergence across varying levels of optimization difficulty. These characteristics make the method broadly applicable to nonlinear data analysis and other complex optimization tasks.Fil: Costa, Federico. Universidad Politécnica de Catalunya; EspañaFil: Quintero Marquina, Gustavo Javier. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Tecnologias Emergentes y Ciencias Aplicadas. - Universidad Nacional de San Martin. Instituto de Tecnologias Emergentes y Ciencias Aplicadas.; ArgentinaFil: Riddick, Maximiliano Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Núcleo Consolidado de Matemática Pura y Aplicada; ArgentinaFil: Andrini, Leandro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; ArgentinaFil: Vahnovan, Alejandra Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; ArgentinaFil: Huck Iriart, Cristián. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Tecnologias Emergentes y Ciencias Aplicadas. - Universidad Nacional de San Martin. Instituto de Tecnologias Emergentes y Ciencias Aplicadas.; ArgentinaScientific Research and Community2025-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/278863Costa, Federico; Quintero Marquina, Gustavo Javier; Riddick, Maximiliano Luis; Andrini, Leandro Ruben; Vahnovan, Alejandra Valeria; et al.; Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis; Scientific Research and Community; Journal of Mathematical & Computer Applications; 4; 4; 8-2025; 1-62754-6705CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinescientificresearch.com/journals/jmca/abstract/selfcooling-simulated-annealing-scsa-algorithm-for-nonlinear-least-squares-data-analysis-6739.htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-01-14T11:44:15Zoai:ri.conicet.gov.ar:11336/278863instacron: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:34982026-01-14 11:44:15.301CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
title Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
spellingShingle Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
Costa, Federico
MONTE CARLO
DATA ANALYSIS
PARAMETRIC FUNCTIONS
SIMULATED ANNEALING
title_short Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
title_full Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
title_fullStr Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
title_full_unstemmed Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
title_sort Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
dc.creator.none.fl_str_mv Costa, Federico
Quintero Marquina, Gustavo Javier
Riddick, Maximiliano Luis
Andrini, Leandro Ruben
Vahnovan, Alejandra Valeria
Huck Iriart, Cristián
author Costa, Federico
author_facet Costa, Federico
Quintero Marquina, Gustavo Javier
Riddick, Maximiliano Luis
Andrini, Leandro Ruben
Vahnovan, Alejandra Valeria
Huck Iriart, Cristián
author_role author
author2 Quintero Marquina, Gustavo Javier
Riddick, Maximiliano Luis
Andrini, Leandro Ruben
Vahnovan, Alejandra Valeria
Huck Iriart, Cristián
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv MONTE CARLO
DATA ANALYSIS
PARAMETRIC FUNCTIONS
SIMULATED ANNEALING
topic MONTE CARLO
DATA ANALYSIS
PARAMETRIC FUNCTIONS
SIMULATED ANNEALING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A Self-Cooling Simulated Annealing (SCSA) algorithm is introduced for the optimization of nonlinear least-squares problems. In contrast to conventional simulated annealing techniques that require a predefined cooling schedule, the SCSA algorithm autonomously regulates system temperature based on the lowest figure of merit (ie. χ²) achieved at each iteration. The algorithm incorporates two key enhancements to improve efficiency: the separation of linear and nonlinear parameters, which reduces the dimensionality of the stochastic search space, and an adaptive Gaussian sampling mechanism that dynamically updates parameter-specific variances based on recent optimization history. A thermal resistance parameter (K) regulates the cooling rate and can be adjusted according to problem complexity. Performance benchmarking against standard Monte Carlo and gradient-based methods demonstrates that SCSA offers greater robustness in avoiding local minima and provides reliable convergence across varying levels of optimization difficulty. These characteristics make the method broadly applicable to nonlinear data analysis and other complex optimization tasks.
Fil: Costa, Federico. Universidad Politécnica de Catalunya; España
Fil: Quintero Marquina, Gustavo Javier. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Tecnologias Emergentes y Ciencias Aplicadas. - Universidad Nacional de San Martin. Instituto de Tecnologias Emergentes y Ciencias Aplicadas.; Argentina
Fil: Riddick, Maximiliano Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Núcleo Consolidado de Matemática Pura y Aplicada; Argentina
Fil: Andrini, Leandro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; Argentina
Fil: Vahnovan, Alejandra Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; Argentina
Fil: Huck Iriart, Cristián. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Tecnologias Emergentes y Ciencias Aplicadas. - Universidad Nacional de San Martin. Instituto de Tecnologias Emergentes y Ciencias Aplicadas.; Argentina
description A Self-Cooling Simulated Annealing (SCSA) algorithm is introduced for the optimization of nonlinear least-squares problems. In contrast to conventional simulated annealing techniques that require a predefined cooling schedule, the SCSA algorithm autonomously regulates system temperature based on the lowest figure of merit (ie. χ²) achieved at each iteration. The algorithm incorporates two key enhancements to improve efficiency: the separation of linear and nonlinear parameters, which reduces the dimensionality of the stochastic search space, and an adaptive Gaussian sampling mechanism that dynamically updates parameter-specific variances based on recent optimization history. A thermal resistance parameter (K) regulates the cooling rate and can be adjusted according to problem complexity. Performance benchmarking against standard Monte Carlo and gradient-based methods demonstrates that SCSA offers greater robustness in avoiding local minima and provides reliable convergence across varying levels of optimization difficulty. These characteristics make the method broadly applicable to nonlinear data analysis and other complex optimization tasks.
publishDate 2025
dc.date.none.fl_str_mv 2025-08
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/11336/278863
Costa, Federico; Quintero Marquina, Gustavo Javier; Riddick, Maximiliano Luis; Andrini, Leandro Ruben; Vahnovan, Alejandra Valeria; et al.; Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis; Scientific Research and Community; Journal of Mathematical & Computer Applications; 4; 4; 8-2025; 1-6
2754-6705
CONICET Digital
CONICET
url http://hdl.handle.net/11336/278863
identifier_str_mv Costa, Federico; Quintero Marquina, Gustavo Javier; Riddick, Maximiliano Luis; Andrini, Leandro Ruben; Vahnovan, Alejandra Valeria; et al.; Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis; Scientific Research and Community; Journal of Mathematical & Computer Applications; 4; 4; 8-2025; 1-6
2754-6705
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://onlinescientificresearch.com/journals/jmca/abstract/selfcooling-simulated-annealing-scsa-algorithm-for-nonlinear-least-squares-data-analysis-6739.html
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 Scientific Research and Community
publisher.none.fl_str_mv Scientific Research and Community
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
_version_ 1854320807640039424
score 13.113929

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