TY - JOUR
T1 - Kriging-Based Space Exploration Global Optimization Method in Aerodynamic Design
AU - Zhang, Wei
AU - Gao, Zhenghong
AU - Wang, Chao
AU - Xia, Lu
N1 - Publisher Copyright:
© 2023 Wei Zhang et al.
PY - 2023
Y1 - 2023
N2 - For complicated aerodynamic design problems, the efficient global optimization method suffered from the defect of the incorrect portrayal of the design space, resulting in bad global convergence and efficiency performance. To this end, a Kriging-based global optimization method, named the Kriging-based space exploration method (KSE), was proposed in this paper. It selected multiple promising local minima and classified them into partially and fully explored minima in terms of the fitting quality of the surrogate model. Then, the partially explored minima would be furtherly exploited. During the local search, an enhanced trust-region method was adopted to make deep exploitation. By combining local and global searches, the proposed method could improve the fitting quality of the surrogate model and the optimization efficiency. The KSE was compared to other global surrogate-based optimization methods on 12 bound-constrained testing functions with 2 to 16 design variables and 2 aerodynamic optimization problems with 24 to 77 design variables. The results indicated that the KSE generally took fewer function evaluations to find the global optima or reach the target value in most test problems, holding better efficiency and robustness.
AB - For complicated aerodynamic design problems, the efficient global optimization method suffered from the defect of the incorrect portrayal of the design space, resulting in bad global convergence and efficiency performance. To this end, a Kriging-based global optimization method, named the Kriging-based space exploration method (KSE), was proposed in this paper. It selected multiple promising local minima and classified them into partially and fully explored minima in terms of the fitting quality of the surrogate model. Then, the partially explored minima would be furtherly exploited. During the local search, an enhanced trust-region method was adopted to make deep exploitation. By combining local and global searches, the proposed method could improve the fitting quality of the surrogate model and the optimization efficiency. The KSE was compared to other global surrogate-based optimization methods on 12 bound-constrained testing functions with 2 to 16 design variables and 2 aerodynamic optimization problems with 24 to 77 design variables. The results indicated that the KSE generally took fewer function evaluations to find the global optima or reach the target value in most test problems, holding better efficiency and robustness.
UR - http://www.scopus.com/inward/record.url?scp=85148236581&partnerID=8YFLogxK
U2 - 10.1155/2023/4493349
DO - 10.1155/2023/4493349
M3 - 文章
AN - SCOPUS:85148236581
SN - 1687-5966
VL - 2023
JO - International Journal of Aerospace Engineering
JF - International Journal of Aerospace Engineering
M1 - 4493349
ER -