超声速低声爆布局分层优化方法

声爆抑制是发展新一代超声速民机必须突破的关键技术。总体布局参数的合理设计可以使飞行器具有良好的声爆特性。为了提高全局进化算法在布局设计中的优化效率，提出一种基于数据挖掘的分层优化方法，利用数据挖掘中的决策树算法提取设计知识，获得设计变量分层信息，指导低声爆布局分层优化；针对某超声速低声爆飞行器，选取后掠角、上反角、展弦比、梢根比、长细比五个总体布局参数作为设计变量，开展分层优化数值实验，并与一体化优化形成对比验证。结果表明：分层优化方法能够搜索到与一体化优化高度吻合的最优解，分层优化的收敛速度显著高于一体化优化，且对随机寻优历程的表现更稳健。

Hierarchical Optimization Method of Supersonic Low Sonic Boom Configuration

Sonic boom suppression is a key technology that must be broken through in the development of a new generation of supersonic civil aircraft. The reasonable optimization of configuration parameters can make the aircraft have good sonic boom characteristics. In order to break through the bottleneck of evolutionary algorithm in optimizing large-scale design variables, a hierarchical optimization method based on data mining is proposed, the decesion tree algorithm in data mining is used to extract the design knowledge, obtain the hierarchical information of design variables, and guide the configuration optimization of low sonic boom aircraft. For a low boom supersonic aircraft, five configuration parameters, including sweep angle, aspect ratio, taper ratio, dihedral angle and fuselage slenderness ratio, are selected as design variables, carry out numerical experiments of hierarchical optimization, and compare with the integrated optimization. Because the optimization process of evolutionary algorithm is random, 100 numerical experiments are carried out to obtain statistical results.The results shows that the hierarchical optimization can obtain the optimal solution consistent with the integrated optimization, and the convergence speed of hierarchical optimization is significantly faster than that of integrated optimization, and the performance of different optimization processes is more robust.