Application of a Dirichlet Distribution-Based Ensemble Surrogate Model in Aerodynamic Optimization

Abstract

Surrogate models have been widely applied in the aerodynamic optimization of aircrafts, whereas the traditional individual surrogate models have the defects of low robustness and applicability. In this study, a novel ensemble surrogate model is proposed and applied in the multi-objective optimization of the airfoil. The backpropagation neural network, deep belief network, and kriging surrogate models are selected as the member surrogate models, and the Dirichlet distribution strategy is introduced to adaptively generate the weights of the member surrogate models in constructing the ensemble surrogate model. An improved multi-objective particle swarm optimization (MOPSO) framework is established by employing the [Formula: see text]-stable distribution function to enhance the global convergence rate of the algorithm. Based on the improved MOPSO framework in which the ensemble surrogate model is embedded, the multi-objective optimization of the airfoil is conducted. The results indicate that the proposed ensemble-surrogate-model-based optimization obtains better aerodynamic performance of the airfoil under multiple operating conditions, compared to the individual-surrogate-model-based optimization.