Problem Formulations for Simulation-based Design Optimization using Statistical Surrogates and Direct Search
Abstract: Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem considered at each search step of the Mesh Adaptive Direct Search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on twenty analytical benchmark problems and two simulation-based multidisciplinary design optimization problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.
Keywords: Simulation-based design optimization; mesh adaptive direct search (MADS); surrogate management framework; statistical surrogates.
Category 1: Convex and Nonsmooth Optimization (Nonsmooth Optimization )
Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )
Category 3: Applications -- Science and Engineering
Citation: Technical report, Les Cahiers du GERAD G-2014-04, 2014.
Entry Submitted: 02/19/2014
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