Parallel surrogate-based optimization using Mesh Adaptive Direct Search

, , and

BibTeX reference

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.

, 29 pages

This cahier was revised in December 2020

Research Axis

Research application


G2038R.pdf (600 KB)