Improving efficiency of a multistart with interrupted Hooke-and-Jeeves filter search for solving MINLP problems

By Fernandes, F.P.; Costa, M.F.P.; Rocha, A.M.A.C.; Fernandes, E.M.G&a

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2016

Abstract

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 -neighborhood of an already computed minimizer. Preliminary numerical results are included.

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