基于代理模型全局优化的自适应参数化方法

The results of the airfoil aerodynamic/stealth design with different numbers of design variables are compared and analyzed, revealing considerable impact of the design variable configuration on the results. Simple increase in the design variables cannot guarantee ideal results. This paper proposes an adaptive parameterization method for surrogate-based optimization. Using the global sensitivity analysis method and the element effect method, it obtains the sensitive information about the objectives in the design space to add design variables. The knot insertion algorithm is adopted to reconstruct samples in the high-dimensional space, avoiding the computational cost of resampling. Compared with the traditional fixed-dimensional method, the adaptive parameterization method expands the dimension in the sensitive area of the design space. The expanded design space can more accurately describe the shape and reflect the changing trend of the objective function. Therefore, the proposed method can significantly improve the quality and efficiency of optimization.