@article{LI2024, 
author = {Haoda LI and Teng LONG and Renhe SHI and Nianhui YE},
title = {Kriging-based mixed?integer optimization method using sample mapping mechanism for flight vehicle design},
year = {2024},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {45},
number = {3},
pages = {228726},
keywords = {Kriging, discrete-continuous mixed optimization, approximate optimization, expected improvement, significant sampling space},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2023.28726},
doi = {10.7527/S1000-6893.2023.28726},
abstract = {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.}
}