Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search

• URL: http://arxiv.org/abs/2107.12421v1
• Date: Mon, 26 Jul 2021 18:28:56 GMT
• Title: Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search
• Authors: Bastien Talgorn, St\'ephane Alarie, and Michael Kokkolaras
• Abstract summary: We present a method that employs surrogate models and concurrent computing at the search step of the mesh adaptive direct search (MADS) algorithm. We conduct numerical experiments to assess the performance of the modified MADS algorithm with respect to available CPU resources.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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