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The mesh adaptive direct search algorithm with treed Gaussian process surrogates

Robert B. Gramacy (rbgramacy***at***chicagobooth.edu)
S├ębastien Le Digabel (Sebastien.Le.Digabel***at***gerad.ca)

Abstract: This work introduces the use of the treed Gaussian process (TGP) as a surrogate model within the mesh adaptive direct search (MADS) framework for constrained blackbox optimization. It extends the surrogate management framework (SMF) to nonsmooth optimization under general constraints. MADS uses TGP in two ways: one, as a surrogate for blackbox evaluations; and two, to evaluate statistical criteria such as the expected improvement and the average reduction in variance. The efficiency of the method is tested on three problems: a synthetic one with many local optima; one real application from a chemical engineering simulator for styrene production; and one from contaminant cleanup and hydrology. In all three cases we show that the TGP surrogate is preferable to a quadratic model and to MADS without any surrogate at all.

Keywords: Blackbox optimization, treed Gaussian processes (TGP), mesh adaptive direct search (MADS), surrogate management framework (SMF).

Category 1: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Pacific Journal of Optimization, 11(3), p. 419-447, 2015.


Entry Submitted: 07/06/2011
Entry Accepted: 07/06/2011
Entry Last Modified: 08/13/2015

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