Particle Swarm Optimization of Aerodynamic Shapes with Nonuniform Shape Parameter–Based Radial Basis Function

Abstract

Efficient global optimization of aerodynamic shapes with the high-fidelity method is of great importance in the design process of modern aircrafts. In this study, modifications are made to the particle swarm optimization (PSO) algorithm and the radial basis function (RBF) model for further improvements of efficiency and accuracy of optimization. Specifically, a PSO algorithm with randomly distributed cognitive and social parameters, exponential decrease of maximum velocity and inertia weight, and periodic mutation of particle position is proposed. Furthermore, a nonuniform shape parameters strategy is introduced for the RBF surrogate model. Validations on test functions show that the new PSO has remarkable speed of convergence, and the new RBF model has superior approximation accuracy. The PSO algorithm and RBF model are then combined to construct the computational fluid dynamics (CFD)–based optimization framework. Finally, optimizations of transonic airfoil and supersonic launch vehicle are performed and results show that the drag coefficients in the two cases are significantly reduced (14 and 15%, respectively). The successful applications also indicate that the proposed PSO and RBF as a whole have apparent advantages over their original versions, and the optimization framework is effective and practical for design of aerodynamic shapes.