  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.
