Abstract
The paper details a comparative analysis of different models able to provide a fast response within a surrogate-based shape optimization process. Kriging, Radial Basis Function Network (RBFN) and Proper Orthogonal Decomposition in combination with RBFNs (POD+RBFN) are employed as fitness function evaluators within the framework of evolutionary algorithms (EAs). The surrogate-assisted optimization consists of initializing the surrogate with space-filling samples, improving the accuracy by adding a series of “smart” samples through specifically designed in-fill criteria and finally optimizing on the surrogate. The test case is represented by the large scale shape optimization of a transonic wing in viscous flow and in multi-design point conditions. Optimization results obtained with the surrogates by fixing the total computational budget are presented: this procedure allows to make a fair comparison between the models and their performance during the optimization process.
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Iuliano, E. (2019). A Comparative Evaluation of Surrogate Models for Transonic Wing Shape Optimization. In: Andrés-Pérez, E., González, L., Periaux, J., Gauger, N., Quagliarella, D., Giannakoglou, K. (eds) Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-89890-2_11
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