Kriging-based mixed?integer optimization method using sample mapping mechanism for flight vehicle design

To deal with the problems of high computational cost and poor global convergence that often exist in discretecontinuous 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.