基于样本映射与动态 Kriging 的飞行器离散连续优化方法

To deal with the problems of high computational cost and poor global convergence that often exist in discrete-continuous mixed optimization of complex flight vehicle systems，a Sample Mapping and Dynamic Kriging based Discrete-Continuous Mixed Optimization method（SMDK-DC）is proposed. In this method，time-consuming simulation model is replaced by Kriging surrogate model to reduce computational expenses. A sample point mapping mechanism based on generalized Manhattan distance criterion is also proposed to efficiently generate uniformly-distributed real sample points in continuous-discrete domain. Expected improvement criteria is combined with significant sampling space to identify new sample points，update Kriging continuously and dynamically，and guide the rapid convergence of the discrete-continuous optimization process. Benchmark cases show that compared with international methods such as SOMI and NOMAD，SMDK-DC has significant advantages in global convergence and robustness. Finally，SMDK-DC is used for solving a multidisciplinary design optimization problem of solid rocket motor. The method，on the premise of satisfying all the constraints of the combustion chamber and internal ballistic discipline，leads to a total impulse increase of at least 12. 92%，and the optimization yield is 1. 71% higher than that of SOMI，which verifying the effectiveness and engineering practicability of SMDK-DC.