Optimisation with simulated annealing through regularisation of the target function

Autores
Segura, Enrique Carlos
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
A method is presented for function optimisation that generalises the Simulated Annealing algorithm by applying convolutions of the target function with smooth, infinitely differentiable kernels. Hence the search for a global optimum is performed over a sequence of functions that preserve the structure of the original one and converge to it pointwise. From an experimental point of view, the purpose of this paper was to compare the efficiency of this approach with that of the conventional Simulated Annealing. To do this, the proposed technique was tested both on complex combinatorial (discrete) problems (e.g. the Travelling Salesman Problem) and on the search of global minima for continuous functions. In some cases, performance was improved in terms of final results, while in other ones, even if no improvements were attained over the usual Simulated Annealing algorithm, the proposed method shows interesting abilities to provide fairly good approximations in relatively few iterations, i.e. at early stages of the search process.
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Simulated annealing
stochastic optimisation
function regularisation
smooth kernels
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22670

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spelling Optimisation with simulated annealing through regularisation of the target functionSegura, Enrique CarlosCiencias InformáticasSimulated annealingstochastic optimisationfunction regularisationsmooth kernelsA method is presented for function optimisation that generalises the Simulated Annealing algorithm by applying convolutions of the target function with smooth, infinitely differentiable kernels. Hence the search for a global optimum is performed over a sequence of functions that preserve the structure of the original one and converge to it pointwise. From an experimental point of view, the purpose of this paper was to compare the efficiency of this approach with that of the conventional Simulated Annealing. To do this, the proposed technique was tested both on complex combinatorial (discrete) problems (e.g. the Travelling Salesman Problem) and on the search of global minima for continuous functions. In some cases, performance was improved in terms of final results, while in other ones, even if no improvements were attained over the usual Simulated Annealing algorithm, the proposed method shows interesting abilities to provide fairly good approximations in relatively few iterations, i.e. at early stages of the search process.Red de Universidades con Carreras en Informática2006-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1301-1306http://sedici.unlp.edu.ar/handle/10915/22670enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:07Zoai:sedici.unlp.edu.ar:10915/22670Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:08.164SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Optimisation with simulated annealing through regularisation of the target function
title Optimisation with simulated annealing through regularisation of the target function
spellingShingle Optimisation with simulated annealing through regularisation of the target function
Segura, Enrique Carlos
Ciencias Informáticas
Simulated annealing
stochastic optimisation
function regularisation
smooth kernels
title_short Optimisation with simulated annealing through regularisation of the target function
title_full Optimisation with simulated annealing through regularisation of the target function
title_fullStr Optimisation with simulated annealing through regularisation of the target function
title_full_unstemmed Optimisation with simulated annealing through regularisation of the target function
title_sort Optimisation with simulated annealing through regularisation of the target function
dc.creator.none.fl_str_mv Segura, Enrique Carlos
author Segura, Enrique Carlos
author_facet Segura, Enrique Carlos
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Simulated annealing
stochastic optimisation
function regularisation
smooth kernels
topic Ciencias Informáticas
Simulated annealing
stochastic optimisation
function regularisation
smooth kernels
dc.description.none.fl_txt_mv A method is presented for function optimisation that generalises the Simulated Annealing algorithm by applying convolutions of the target function with smooth, infinitely differentiable kernels. Hence the search for a global optimum is performed over a sequence of functions that preserve the structure of the original one and converge to it pointwise. From an experimental point of view, the purpose of this paper was to compare the efficiency of this approach with that of the conventional Simulated Annealing. To do this, the proposed technique was tested both on complex combinatorial (discrete) problems (e.g. the Travelling Salesman Problem) and on the search of global minima for continuous functions. In some cases, performance was improved in terms of final results, while in other ones, even if no improvements were attained over the usual Simulated Annealing algorithm, the proposed method shows interesting abilities to provide fairly good approximations in relatively few iterations, i.e. at early stages of the search process.
Red de Universidades con Carreras en Informática
description A method is presented for function optimisation that generalises the Simulated Annealing algorithm by applying convolutions of the target function with smooth, infinitely differentiable kernels. Hence the search for a global optimum is performed over a sequence of functions that preserve the structure of the original one and converge to it pointwise. From an experimental point of view, the purpose of this paper was to compare the efficiency of this approach with that of the conventional Simulated Annealing. To do this, the proposed technique was tested both on complex combinatorial (discrete) problems (e.g. the Travelling Salesman Problem) and on the search of global minima for continuous functions. In some cases, performance was improved in terms of final results, while in other ones, even if no improvements were attained over the usual Simulated Annealing algorithm, the proposed method shows interesting abilities to provide fairly good approximations in relatively few iterations, i.e. at early stages of the search process.
publishDate 2006
dc.date.none.fl_str_mv 2006-10
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
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format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22670
url http://sedici.unlp.edu.ar/handle/10915/22670
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
1301-1306
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
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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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