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.
@article{arxiv.2107.12421,
title = {Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search},
author = {Bastien Talgorn and Stéphane Alarie and Michael Kokkolaras},
journal= {arXiv preprint arXiv:2107.12421},
year = {2021}
}