Combining Cross-Entropy and MADS Methods for Inequality Constrained Global Optimization

Charles Audet (), Jean Bigeon () and Romain Couderc ()
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Charles Audet: École Polytechnique de Montréal
Jean Bigeon: LS2N
Romain Couderc: École Polytechnique de Montréal

SN Operations Research Forum, 2021, vol. 2, issue 3, 1-26

Abstract: Abstract This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with the Cross-Entropy (CE) method for nonsmooth constrained optimization. The CE method is used as an exploration step by the MADS algorithm. The result of this combination retains the convergence properties of MADS and allows an efficient exploration in order to move away from local minima. The CE method samples trial points according to a multivariate normal distribution whose mean and standard deviation are calculated from the best points found so far. Numerical experiments show the efficiency of this method compared to other global optimization heuristics. Moreover, applied on complex engineering test problems, this method allows an important improvement to reach the feasible region and to escape local minima.

Keywords: Cross-entropy; MADS; Global optimization; Derivative-free optimization; Blackbox optimization; Constrained optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s43069-021-00075-y

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