Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/14682

TitleAssessment of a hybrid approach for nonconvex constrained MINLP problems
Author(s)Costa, M. Fernanda P.
Fernandes, Florbela P.
Fernandes, Edite Manuela da G. P.
KeywordsMixed-integer programming
Genetic algorithm
Branch-and-bound
Issue date2011
Abstract(s)A methodology to solve nonconvex constrained mixed-integer nonlinear programming (MINLP) problems is presented. A MINLP problem is one where some of the variables must have only integer values. Since in most applications of the industrial processes, some problem variables are restricted to take discrete values only, there are real practical problems that are modeled as nonconvex constrained MINLP problems. An efficient deterministic method for solving nonconvex constrained MINLP may be obtained by using a clever extension of Branch-and-Bound (B&B) method. When solving the relaxed nonconvex nonlinear programming subproblems that arise in the nodes of a tree in a B&B algorithm, using local search methods, only convergence to local optimal solutions is guaranteed. Pruning criteria cannot be used to avoid an exhaustive search in the search space. To address this issue, we propose the use of a genetic algorithm to promote convergence to a global optimum of the relaxed nonconvex NLP subproblem. We present some numerical experiments with the proposed algorithm.
TypeConference paper
URIhttp://hdl.handle.net/1822/14682
Publisher versionhttp://gsii.usal.es/~CMMSE/index.php?option=com_content&task=view&id=15&Itemid=16
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review
LES/ALG - Textos completos em actas de encontros científicos internacionais com arbitragem

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