Kriging-Based Variable Screening Method for Aircraft Optimization Problems with Expensive Functions

The computational complexity of airfoil optimization for aircraft wing designs typically involves high-dimensional parameter spaces defined by geometric variables, where each Computational Fluid Dynamics (CFD) simulation cycle may require significant processing resources. Therefore, performing variable selection to identify influential inputs becomes crucial for minimizing the number of necessary model evaluations, particularly when dealing with complex systems exhibiting nonlinear and poorly understood input–output relationships. As a result, it is desirable to use fewer samples to determine the influential inputs to achieve a simple, more efficient optimization process. This article provides a systematic, novel approach to solving aircraft optimization problems. Initially, a Kriging-based variable screening method (KRG-VSM) is proposed to determine the active inputs using a ikelihood-based screening method, and new stopping criteria for KRG-VSM are proposed and discussed. A genetic algorithm (GA) is employed to achieve the global optimum of the log-likelihood function. Subsequently, the airfoil optimization is conducted using the identified active design variables. According to the results, the Kriging-based variable screening method could select all the active inputs with a few samples. The Kriging-based variable screening method is then tested on the numerical benchmarks and applied to the airfoil aerodynamic optimization problem. Applying the variables screening technique can enhance the efficiency of the airfoil optimization process under acceptable accuracy.