Comparison of Surrogate Models in a Multidisciplinary Optimization Framework for Wing Design

The replacement of the analysis portion of an optimization problem by its equivalent metamodel usually results in a lower computational cost. In this paper, a conventional nonapproximative approach is compared against three different metamodels: quadratic-interpolation-based response surfaces, Kriging, and artificial neural networks. The results obtained from the solution of four different case studies based on aircraft design problems reinforces the idea that quadratic interpolation is only well-suited to very simple problems. At higher dimensionality, the usage of the more complex Kriging and artificial neural networks models may result in considerable performance benefits.