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Extensions of a multistart clustering algorithm for constrained global optimization problems / José-Oscar H. Sendín in Industrial & engineering chemistry research, Vol. 48 N° 6 (Mars 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 6 (Mars 2009) . - pp. 3014–3023 Titre : Extensions of a multistart clustering algorithm for constrained global optimization problems Type de document : texte imprimé Auteurs : José-Oscar H. Sendín, Auteur ; Banga, Julio R., Auteur ; Tibor Csendes, Auteur Année de publication : 2009 Article en page(s) : pp. 3014–3023 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Nonlinear  programming  problems  Multistart  clustering  algorithm Résumé : Here, we consider the solution of constrained global optimization problems, such as those arising from the fields of chemical and biosystems engineering. These problems are frequently formulated (or can be transformed to) nonlinear programming problems (NLPs) subject to differential−algebraic equations (DAEs). In this work, we extend a popular multistart clustering algorithm for solving these problems, incorporating new key features including an efficient mechanism for handling constraints and a robust derivative-free local solver. The performance of this new method is evaluated by solving a collection of test problems, including several challenging case studies from the (bio)process engineering area. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800319m [article] Extensions of a multistart clustering algorithm for constrained global optimization problems [texte imprimé] / José-Oscar H. Sendín, Auteur ; Banga, Julio R., Auteur ; Tibor Csendes, Auteur . - 2009 . - pp. 3014–3023. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 6 (Mars 2009) . - pp. 3014–3023 Mots-clés : Nonlinear  programming  problems  Multistart  clustering  algorithm Résumé : Here, we consider the solution of constrained global optimization problems, such as those arising from the fields of chemical and biosystems engineering. These problems are frequently formulated (or can be transformed to) nonlinear programming problems (NLPs) subject to differential−algebraic equations (DAEs). In this work, we extend a popular multistart clustering algorithm for solving these problems, incorporating new key features including an efficient mechanism for handling constraints and a robust derivative-free local solver. The performance of this new method is evaluated by solving a collection of test problems, including several challenging case studies from the (bio)process engineering area. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800319m