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Robust optimization of noisy blackbox problems using the Mesh Adaptive Direct Search algorithm

Charles Audet (Charles.Audet***at***gerad.ca)
Amina Ihaddadene (amina.ihaddadene***at***polymtl.ca)
Sébastien Le Digabel (Sebastien.Le.Digabel***at***gerad.ca)
Christophe Tribes (christophe.tribes***at***polymtl.ca)

Abstract: Blackbox optimization problems are often contaminated with numerical noise, and direct search methods such as the Mesh Adaptive Direct Search (MADS) algorithm may get stuck at solutions artificially created by the noise. We propose a way to smooth out the objective function of an unconstrained problem using previously evaluated function evaluations, rather than resampling points. The new algorithm, called Robust-MADS is applied to noisy problems from the literature.

Keywords: Robust optimization; Direct search; Blackbox optimization; MADS.

Category 1: Applications -- OR and Management Sciences

Category 2: Nonlinear Optimization

Citation: Optimization Letters, 12(4), p. 675-689, 2018. Doi: 10.1007/s11590-017-1226-6


Entry Submitted: 07/19/2016
Entry Accepted: 07/19/2016
Entry Last Modified: 05/25/2018

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