A surrogate-based optimization method for mixed-variable aircraft design

Aircraft optimization design problems are mostly computationally intensive. These complicated problems probably contain mixed-variables, while most research has focused on continuous variables. This article sets up a mixed-variable surrogate-based optimization algorithm framework that includes a mixed-variable experiment design method and an inaccurate infilling method. The mixed-variable experiment design method combines improved successive local enumeration with enhanced stochastic evolution to deal directly with discrete variables. The inaccurate infilling method tends to find points with better fitness value and relatively low sample density so as to balance exploration and exploitation. Several numerical functions and a mixed-variable solid rocket motor performance matching design problem are solved using the modified surrogate-based optimization method. The results indicate that the proposed method is competitive compared with other heuristic algorithms and surrogate-based algorithms, and can deal with mixed-variable aircraft design problems effectively.

Automated summary

A mixed-variable surrogate-based optimization algorithm framework is proposed to effectively address mixed-variable aircraft design problems, showing competitive results compared to other heuristic algorithms and surrogate-based algorithms.