Documents disponibles écrits par cet auteur

Scatter tabu search for multiobjective clustering problems / R. Caballero in Journal of the operational research society (JORS), Vol. 62 N° 11 (Novembre 2011) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 62 N° 11 (Novembre 2011) . - pp. 2034–2046 Titre : Scatter tabu search for multiobjective clustering problems Type de document : texte imprimé Auteurs : R. Caballero, Auteur ; M. Laguna, Auteur ; R. Martí, Auteur Année de publication : 2011 Article en page(s) : pp. 2034–2046 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : SSPMO  Multiobjective  optimization  Scatter  and  tabu  search  Combinatorial  data  analysis  Partitioning Index. décimale : 001.424 Résumé : We propose a hybrid heuristic procedure based on scatter search and tabu search for the problem of clustering objects to optimize multiple criteria. Our goal is to search for good approximations of the efficient frontier for this class of problems and provide a means for improving decision making in multiple application areas. Our procedure can be viewed as an extension of SSPMO (a scatter search application to nonlinear multiobjective optimization) to which we add new elements and strategies specially suited for combinatorial optimization problems. Clustering problems have been the subject of numerous studies; however, most of the work has focused on single-objective problems. Clustering using multiple criteria and/or multiple data sources has received limited attention in the operational research literature. Our scatter tabu search implementation is general and tackles several problems classes within this area of combinatorial data analysis. We conduct extensive experimentation to show that our method is capable of delivering good approximations of the efficient frontier for improved analysis and decision making. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n11/abs/jors2010180a.html [article] Scatter tabu search for multiobjective clustering problems [texte imprimé] / R. Caballero, Auteur ; M. Laguna, Auteur ; R. Martí, Auteur . - 2011 . - pp. 2034–2046. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 11 (Novembre 2011) . - pp. 2034–2046 Mots-clés : SSPMO  Multiobjective  optimization  Scatter  and  tabu  search  Combinatorial  data  analysis  Partitioning Index. décimale : 001.424 Résumé : We propose a hybrid heuristic procedure based on scatter search and tabu search for the problem of clustering objects to optimize multiple criteria. Our goal is to search for good approximations of the efficient frontier for this class of problems and provide a means for improving decision making in multiple application areas. Our procedure can be viewed as an extension of SSPMO (a scatter search application to nonlinear multiobjective optimization) to which we add new elements and strategies specially suited for combinatorial optimization problems. Clustering problems have been the subject of numerous studies; however, most of the work has focused on single-objective problems. Clustering using multiple criteria and/or multiple data sources has received limited attention in the operational research literature. Our scatter tabu search implementation is general and tackles several problems classes within this area of combinatorial data analysis. We conduct extensive experimentation to show that our method is capable of delivering good approximations of the efficient frontier for improved analysis and decision making. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n11/abs/jors2010180a.html

The challenge of optimizing expensive black boxes / M. Laguna in Journal of the operational research society (JORS), Vol. 61 N° 1 (Issue spécial) (Janvier 2010) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 61 N° 1 (Issue spécial) (Janvier 2010) . - pp. 53–67 Titre : The challenge of optimizing expensive black boxes : a scatter search/rough set theory approach Type de document : texte imprimé Auteurs : M. Laguna, Auteur ; J. Molina, Auteur ; F. Pérez, Auteur Année de publication : 2011 Article en page(s) : pp. 53–67 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Black-box  optimization  Simulation  optimization  Scatter  search  Rough  sets Index. décimale : 001.424 Résumé : There is renewed interest in the development of effective and efficient methods for optimizing models of which the optimizer has no structural knowledge. This is what in the literature is referred to as optimization of black boxes. In particular, we address the challenge of optimizing expensive black boxes, that is, those that require a significant computational effort to be evaluated. We describe the use of rough set theory within a scatter search framework, with the goal of identifying high-quality solutions with a limited number of objective function evaluations. The rough set strategies that we developed take advantage of the information provided by the best and diverse solutions found during the search, in order to define areas of the solution space that are promising for search intensification. We test our procedure on a set of 92 nonlinear multimodal functions of varied complexity and size and compare the results with a state-of-the-art procedure based on particle swarm optimization. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n1/abs/jors2009124a.html [article] The challenge of optimizing expensive black boxes : a scatter search/rough set theory approach [texte imprimé] / M. Laguna, Auteur ; J. Molina, Auteur ; F. Pérez, Auteur . - 2011 . - pp. 53–67. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 61 N° 1 (Issue spécial) (Janvier 2010) . - pp. 53–67 Mots-clés : Black-box  optimization  Simulation  optimization  Scatter  search  Rough  sets Index. décimale : 001.424 Résumé : There is renewed interest in the development of effective and efficient methods for optimizing models of which the optimizer has no structural knowledge. This is what in the literature is referred to as optimization of black boxes. In particular, we address the challenge of optimizing expensive black boxes, that is, those that require a significant computational effort to be evaluated. We describe the use of rough set theory within a scatter search framework, with the goal of identifying high-quality solutions with a limited number of objective function evaluations. The rough set strategies that we developed take advantage of the information provided by the best and diverse solutions found during the search, in order to define areas of the solution space that are promising for search intensification. We test our procedure on a set of 92 nonlinear multimodal functions of varied complexity and size and compare the results with a state-of-the-art procedure based on particle swarm optimization. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n1/abs/jors2009124a.html