TOWARDS AUTOMATIC PARAMETER SELECTION FOR MULTI-FIDELITY SURROGATE-BASED OPTIMIZATION

The performance of surrogate-based optimization is highly affected by how the surrogate training set is defined. This is especially true for multi-fidelity surrogate models, where different training sets exist for each fidelity. Adaptive sampling methods have been developed to improve the fitting capabilities of surrogate models, avoiding to define an a priori design of experiments, adding training points only where necessary or most useful (i.e., providing the highest knowledge gain) to the optimization process. Nevertheless, the efficiency of the adaptive sampling is highly affected by its initialization. The paper presents and discusses a novel initialization strategy with a limited training set for adaptive sampling. The proposed strategy aims to reduce the computational cost of evaluating the initial training set. Furthermore, it allows the surrogate model to adapt more freely to the data. In this work, the proposed approach is applied to single- and multi-fidelity stochastic radial basis functions for an analytical test problem and the shape optimization of a NACA hydrofoil. Numerical results show that the results of the surrogate-based optimization are improved, thanks to a more effective and efficient domain space exploration and a significant reduction of high-fidelity evaluations.