Abstract: For the purpose of reducing the number of the design variables of the wing, the PCA (principal component analysis) technique is employed to improve the CST parameterization method. A database of the wings is established, including geometrical parameters and aerodynamic parameters. Based on the database, the DBN (deep belief network) is established and trained as the surrogate model in the aerodynamic optimization. In order to improve the convergence rate and global searching ability of the standard MOPSO (multi-objective particle swarm optimization) algorithm, an improved MOPSO algorithm is developed based on the α-stable distribution functions. By embedding the DBN surrogate model into the improved MOPSO algorithm, the multi-objective aerodynamic optimizations are performed for the wing of a general aviation airplane. The optimization results indicate that, the computation amount is decreased dramatically by introducing the PCA technique. Numerical validation is conducted for the designed wing configuration, and the results indicate that, comparing with the baseline configuration, the lift coefficients of the designed configuration are increased obviously under multiple states with the drag coefficients not increased.