A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)

We introduce a novel metaheuristic methodology to improve the initialization of a given deterministic or stochastic optimization algorithm. Our objective is to improve the performance of the considered algorithm, called core optimization algorithm, by reducing its number of cost function evaluations...

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Detalles Bibliográficos
Autores: Ivorra, Benjamín Pierre Paul, Mohammadi, Bijan, Ramos Del Olmo, Ángel Manuel
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/23024
Acceso en línea:https://hdl.handle.net/20.500.14352/23024
Access Level:acceso abierto
Palabra clave:519.863
Metaheuristics
Global optimization
Multi-layer line search algorithms
Evolutionary Algorithms
Gradient methods
Investigación operativa (Matemáticas)
1207 Investigación Operativa
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spelling A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)Ivorra, Benjamín Pierre PaulMohammadi, BijanRamos Del Olmo, Ángel Manuel519.863MetaheuristicsGlobal optimizationMulti-layer line search algorithmsEvolutionary AlgorithmsGradient methodsInvestigación operativa (Matemáticas)1207 Investigación OperativaWe introduce a novel metaheuristic methodology to improve the initialization of a given deterministic or stochastic optimization algorithm. Our objective is to improve the performance of the considered algorithm, called core optimization algorithm, by reducing its number of cost function evaluations, by increasing its success rate and by boosting the precision of its results. In our approach, the core optimization is considered as a suboptimization problem for a multi-layer line search method. The approach is presented and implemented for various particular core optimization algorithms: Steepest Descent, Heavy-Ball, Genetic Algorithm, Differential Evolution and Controlled Random Search. We validate our methodology by considering a set of low and high dimensional benchmark problems (i.e., problems of dimension between 2 and 1000). The results are compared to those obtained with the core optimization algorithms alone and with two additional global optimization methods (Direct Tabu Search and Continuous Greedy Randomized Adaptive Search). These latter also aim at improving the initial condition for the core algorithms. The numerical results seem to indicate that our approach improves the performances of the core optimization algorithms and allows to generate algorithms more efficient than the other optimization methods studied here. A Matlab optimization package called ”Global Optimization Platform” (GOP), implementing the algorithms presented here, has been developed and can be downloaded at: http://www.mat.ucm.es/momat/software.htmMathematical Otimization SocietyUniversidad Complutense de Madrid20152015-03-2320152015-03-23journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/23024reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/230242026-06-02T12:44:21Z
dc.title.none.fl_str_mv A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
title A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
spellingShingle A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
Ivorra, Benjamín Pierre Paul
519.863
Metaheuristics
Global optimization
Multi-layer line search algorithms
Evolutionary Algorithms
Gradient methods
Investigación operativa (Matemáticas)
1207 Investigación Operativa
title_short A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
title_full A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
title_fullStr A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
title_full_unstemmed A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
title_sort A Multi-Layer Line Search Method to Improve the Initialization of Optimization Algorithms (Preprint submitted to Optimization Online)
dc.creator.none.fl_str_mv Ivorra, Benjamín Pierre Paul
Mohammadi, Bijan
Ramos Del Olmo, Ángel Manuel
author Ivorra, Benjamín Pierre Paul
author_facet Ivorra, Benjamín Pierre Paul
Mohammadi, Bijan
Ramos Del Olmo, Ángel Manuel
author_role author
author2 Mohammadi, Bijan
Ramos Del Olmo, Ángel Manuel
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 519.863
Metaheuristics
Global optimization
Multi-layer line search algorithms
Evolutionary Algorithms
Gradient methods
Investigación operativa (Matemáticas)
1207 Investigación Operativa
topic 519.863
Metaheuristics
Global optimization
Multi-layer line search algorithms
Evolutionary Algorithms
Gradient methods
Investigación operativa (Matemáticas)
1207 Investigación Operativa
description We introduce a novel metaheuristic methodology to improve the initialization of a given deterministic or stochastic optimization algorithm. Our objective is to improve the performance of the considered algorithm, called core optimization algorithm, by reducing its number of cost function evaluations, by increasing its success rate and by boosting the precision of its results. In our approach, the core optimization is considered as a suboptimization problem for a multi-layer line search method. The approach is presented and implemented for various particular core optimization algorithms: Steepest Descent, Heavy-Ball, Genetic Algorithm, Differential Evolution and Controlled Random Search. We validate our methodology by considering a set of low and high dimensional benchmark problems (i.e., problems of dimension between 2 and 1000). The results are compared to those obtained with the core optimization algorithms alone and with two additional global optimization methods (Direct Tabu Search and Continuous Greedy Randomized Adaptive Search). These latter also aim at improving the initial condition for the core algorithms. The numerical results seem to indicate that our approach improves the performances of the core optimization algorithms and allows to generate algorithms more efficient than the other optimization methods studied here. A Matlab optimization package called ”Global Optimization Platform” (GOP), implementing the algorithms presented here, has been developed and can be downloaded at: http://www.mat.ucm.es/momat/software.htm
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-03-23
2015
2015-03-23
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/23024
url https://hdl.handle.net/20.500.14352/23024
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Mathematical Otimization Society
publisher.none.fl_str_mv Mathematical Otimization Society
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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