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Advisor(s)
Abstract(s)
This paper addresses the problem of solving mixed-integer nonlinear programming (MINLP) problems by a multistart strategy that invokes a derivative-free local search procedure based on a filter set methodology to handle nonlinear constraints. A new concept of componentwise normalized distance aiming to discard randomly generated points that are sufficiently close to other points already used to invoke the local search is analyzed. A variant of the Hooke-and-Jeeves filter algorithm for MINLP is proposed with the goal of interrupting the iterative process if the accepted iterate falls inside an ϵ-neighborhood of an already computed minimizer. Preliminary numerical results are included.
Description
Keywords
Nonconvex MINLP Multistart Hooke-and-Jeeves Filter method
Citation
Fernandes, Florbela P.; Costa, M. Fernanda P.; Rocha, Ana Maria A.C.; Fernandes, Edite M.G.P. (2016). Improving efficiency of a multistart with interrupted hooke-and-jeeves filter search for solving MINLP problems. In Gervais, Osvaldo [et al.] (eds.) 16th International Conference on Computational Science and Its Applications, ICCSA 2016. Beijing. p. 345-358. ISBN 978-3-319-42085-127
Publisher
Springer