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arXiv · 2107.12421

Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search

Bastien Talgorn·Stéphane Alarie·Michael Kokkolaras
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Abstract

We consider computationally expensive blackbox optimization problems and present a method that employs surrogate models and concurrent computing at the search step of the mesh adaptive direct search (MADS) algorithm. Specifically, we solve a surrogate optimization problem using locally weighted scatterplot smoothing (LOWESS) models to find promising candidate points to be evaluated by the blackboxes. We consider several methods for selecting promising points from a large number of points. We conduct numerical experiments to assess the performance of the modified MADS algorithm with respect to available CPU resources by means of five engineering design problems.

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Bastien Talgorn, Stéphane Alarie, Michael Kokkolaras. 2021-07-26. Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search. https://arxiv.org/abs/2107.12421

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