Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search

TL;DR

This paper introduces a parallel surrogate-assisted MADS algorithm that uses LOWESS models to efficiently solve computationally expensive blackbox optimization problems by leveraging concurrent computing and surrogate models.

Contribution

It presents a novel integration of surrogate models with parallel MADS, improving efficiency in solving expensive blackbox optimization problems.

Findings

Enhanced optimization efficiency with parallel computing.

Effective surrogate model integration for candidate point selection.

Successful application to engineering design problems.

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.