Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search
This work addresses optimization challenges in engineering design, but it is incremental as it modifies an existing method.
The paper tackles computationally expensive blackbox optimization problems by integrating surrogate models and parallel computing into the MADS algorithm, resulting in improved performance assessed through five engineering design problems.
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.