# Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search

arXiv: https://arxiv.org/abs/2107.12421 · HTML: https://mlsanity.com/paper/2107.12421/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 26 Jul 2021.

## Abstract (arXiv)

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.