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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22670
Ver los metadatos del registro completo
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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 info:ar-repo/semantics/documentoDeConferencia |
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) |
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application/pdf 1301-1306 |
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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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Universidad Nacional de La Plata |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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