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 (SP) considered at each search step of the mesh adaptive direct search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on 20 analytical benchmark problems and two simulation-based multidisciplinary design optimization (MDO) problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.
Statistical Surrogate Formulations for Simulation-Based Design Optimization
et Génie Industriel,
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 18, 2014; final manuscript received September 9, 2014; published online December 15, 2014. Assoc. Editor: Gary Wang.
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Talgorn, B., Le Digabel, S., and Kokkolaras, M. (February 1, 2015). "Statistical Surrogate Formulations for Simulation-Based Design Optimization." ASME. J. Mech. Des. February 2015; 137(2): 021405. https://doi.org/10.1115/1.4028756
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