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Optimization Letters

, Volume 12, Issue 4, pp 675–689 | Cite as

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

  • Charles Audet
  • Amina Ihaddadene
  • Sébastien Le DigabelEmail author
  • Christophe Tribes
Original Paper

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 a collection of noisy analytical problems from the literature and on an optimization problem to tune the parameters of a trust-region method.

Keywords

Robust optimization Direct search Blackbox optimization MADS 

Notes

Acknowledgements

This work was supported in part by FRQNT Grant 2015-PR-182098 and NSERC Grant RDCPJ 490744-15 in collaboration with Rio Tinto and Hydro-Québec.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Département de mathématiques et génie industriel, École Polytechnique de MontréalGERADMontrealCanada

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