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Hybrid approach for Pareto front expansion in heuristics / Yapicioglu, H. in Journal of the operational research society (JORS), Vol. 62 N° 2 Special issue (Fevrier 2011) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 62 N° 2 Special issue (Fevrier 2011) . - pp. 348–359 Titre : Hybrid approach for Pareto front expansion in heuristics Type de document : texte imprimé Auteurs : Yapicioglu, H., Auteur ; Liu, H., Auteur ; Smith, A. E., Auteur Année de publication : 2011 Article en page(s) : pp. 348–359 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Kriging  General  regression  neural  network  Multi-objective  optimization  Heuristic  search  Hybrid  methods Index. décimale : 001.424 Résumé : Heuristic search can be an effective multi-objective optimization tool; however, the required frequent function evaluations can exhaust computational sources. This paper explores using a hybrid approach with statistical interpolation methods to expand optimal solutions obtained by multiple criteria heuristic search. The goal is to significantly increase the number of Pareto optimal solutions while limiting computational effort. The interpolation approaches studied are kriging and general regression neural networks. This paper develops a hybrid methodology combining an interpolator with a heuristic, and examines performance on several non-linear bi-objective example problems. Computational experience shows this approach successfully expands and enriches the Pareto fronts of multi-objective optimization problems. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n2/abs/jors2010151a.html [article] Hybrid approach for Pareto front expansion in heuristics [texte imprimé] / Yapicioglu, H., Auteur ; Liu, H., Auteur ; Smith, A. E., Auteur . - 2011 . - pp. 348–359. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 2 Special issue (Fevrier 2011) . - pp. 348–359 Mots-clés : Kriging  General  regression  neural  network  Multi-objective  optimization  Heuristic  search  Hybrid  methods Index. décimale : 001.424 Résumé : Heuristic search can be an effective multi-objective optimization tool; however, the required frequent function evaluations can exhaust computational sources. This paper explores using a hybrid approach with statistical interpolation methods to expand optimal solutions obtained by multiple criteria heuristic search. The goal is to significantly increase the number of Pareto optimal solutions while limiting computational effort. The interpolation approaches studied are kriging and general regression neural networks. This paper develops a hybrid methodology combining an interpolator with a heuristic, and examines performance on several non-linear bi-objective example problems. Computational experience shows this approach successfully expands and enriches the Pareto fronts of multi-objective optimization problems. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n2/abs/jors2010151a.html