基于鸽群搜索策略的代理模型结构优化方法

Optimization algorithm of the surrogate model structure based on pigeon swarm search strategy

针对高超声速飞行器系统间耦合严重、设计变量多,在大样本下高超声速飞行器代理模型结构难以确定的问题,本文采用鸽群算法的优化思想提出一种代理模型结构优化方法.该方法可以自主搜索到满足精度和预测性能要求的多项式代理模型结构.通过比较分析,该方法的性能优于Davidson提出的遗传规划算法.进而将其应用于高超声速飞行器全局气动数据拟合和控制器设计,仿真结果表明通过该方法获得的预测数据误差小于5%,具有好的拟合效果,能够满足工程应用要求.

The hypersonic vehicle has problems with multiple systems coupling and multivariate design. Hence, it is difficult to determine the surrogate model structure of the hypersonic vehicle under larger samples. To solve this problem, this paper proposes a surrogate model structure optimization method based on the idea of PIO algorithm. The method can independently search for a polynomial surrogate model structure that satisfies the accuracy and prediction performance requirements. Through comparative analysis,utilizing this method outperforms Davidson’s genetic programming algorithm. Furthermore, it was applied to the global aerodynamic data fitting and control design of a hypersonic vehicle, and the simulation results reveal that the error of the prediction data obtained by this method was less than 5%, which has a good fitting effect and can meet the engineering application requirements.