Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/49095

TitleContinuous relaxation of MINLP problems by penalty functions: a practical comparison
Author(s)Costa, M. Fernanda P.
Rocha, Ana Maria A. C.
Fernandes, Edite Manuela da G. P.
KeywordsContinuous relaxation
Penalty function
DIRECT
MINLP
Issue date2017
PublisherSpringer
JournalLecture Notes in Computer Science
CitationCosta M.F.P., Rocha A.M.A.C., Fernandes E.M.G.P. (2017) Continuous Relaxation of MINLP Problems by Penalty Functions: A Practical Comparison. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science, vol 10406. Springer, Cham
Abstract(s)A practical comparison of penalty functions for globally solving mixed-integer nonlinear programming (MINLP) problems is presented. The penalty approach relies on the continuous relaxation of the MINLP problem by adding a specific penalty term to the objective function. A new penalty algorithm that addresses simultaneously the reduction of the error tolerances for optimality and feasibility, as well as the reduction of the penalty parameter, is designed. Several penalty terms are tested and different penalty parameter update schemes are analyzed. The continuous nonlinear optimization problem is solved by the deterministic DIRECT optimizer. The numerical experiments show that the quality of the produced solutions are satisfactory and that the selected penalties have different performances in terms of efficiency and robustness.
TypeConference paper
URIhttp://hdl.handle.net/1822/49095
ISBN978-3-319-62397-9
DOI10.1007/978-3-319-62398-6_8
ISSN0302-9743
Publisher versionhttps://link.springer.com/chapter/10.1007%2F978-3-319-62398-6_8
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

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