Derivative-free Methods for Mixed-Integer Constrained Optimization Problems (Articolo in rivista)

Type
Label
  • Derivative-free Methods for Mixed-Integer Constrained Optimization Problems (Articolo in rivista) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s10957-014-0617-4 (literal)
Alternative label
  • G. Liuzzi, S. Lucidi, F. Rinaldi (2014)
    Derivative-free Methods for Mixed-Integer Constrained Optimization Problems
    in Journal of optimization theory and applications; Springer, Dordrecht (Paesi Bassi)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • G. Liuzzi, S. Lucidi, F. Rinaldi (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
  • Scopus (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • G. Liuzzi: Istituto di Analisi dei Sistemi ed Informatica (IASI) \"A.Ruberti\", CNR S. Lucidi: Dipartimento di Informatica e Sistemistica \"A. Ruberti\", \"Sapienza\" Università di Roma F. Rinaldi: Dipartimento di Matematica, Università di Padova (literal)
Titolo
  • Derivative-free Methods for Mixed-Integer Constrained Optimization Problems (literal)
Abstract
  • Methods which do not use any derivative information are becoming popular among researchers, since they allow to solve many real-world engineering problems. Such problems are frequently characterized by the presence of discrete variables which can further complicate the optimization process. In this paper, we propose derivative-free algorithms for solving continuously differentiable Mixed Integer NonLinear Programming problems with general nonlinear constraints and explicit handling of bound constraints on the problem variables. We use an exterior penalty approach to handle the general nonlinear constraints and a local search approach to take into account the presence of discrete variables. We show that the proposed algorithms globally converge to points satisfying different necessary optimality conditions. We report a computational experience and a comparison with a well-known derivative-free optimization software package, i.e., NOMAD, on a set of test problems. Furthermore, we employ the proposed methods and NOMAD to solve a real problem concerning the optimal design of an industrial electric motor. This allows to show that the method converging to the better extended stationary points obtains the best solution also from an applicative point of view. (literal)
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