A global optimisation strategy based on the hybrid surrogate model method and the competitive mechanism-based multi-objective particle swarm optimisation (CMOPSO) algorithm was developed to improve the accuracy of the aerodynamic performance optimisation of a high-speed train running in open air without a crosswind. Free-form deformation was used to improve the optimisation efficiency without remodelling or remeshing. The sample points and their responses were obtained via optimal Latin hypercube sampling and computational fluid dynamics (CFD) simulations. The hybrid surrogate model (HSM) was constructed by using the theory of optimal weighted surrogate to combine a polynomial response surface (PRS) model with a radial basis function (RBF) model. Comprehensive error evaluation results indicated that the prediction accuracy achieved with the HSM was higher than that achieved with either the PRS model or the RBF model; thus, the HSM was selected for use in each iteration to approximate the CFD simulation model of the high-speed train in subsequent optimisation. Then, the CMOPSO algorithm was selected as the optimisation algorithm. After optimisation, a series of Pareto-optimal solutions was obtained, and the optimal and original head shapes were compared. The use of the hybrid surrogate model and the CMOPSO algorithm greatly improved the optimisation efficiency.

提出了一种基于混合替代模型方法和基于竞争机制的多目标粒子群优化算法（CMOPSO）的全局优化策略，以提高高速列车在空载条件下空气动力学性能优化的准确性。侧风。自由变形用于提高优化效率，而无需重塑或重新划分网格。样本点及其响应是通过最佳拉丁超立方体采样和计算流体力学（CFD）模拟获得的。利用最优加权代理理论，将多项式响应面（PRS）模型与径向基函数（RBF）模型相结合，构建了混合代理模型（HSM）。综合误差评估结果表明，HSM的预测精度高于PRS模型或RBF模型。因此，选择HSM用于每次迭代，以在随后的优化中近似高速列车的CFD仿真模型。然后，选择CMOPSO算法作为优化算法。优化后，获得了一系列帕累托最优解，并比较了最佳头部形状和原始头部形状。混合代理模型和CMOPSO算法的使用大大提高了优化效率。选择CMOPSO算法作为优化算法。优化后，获得了一系列帕累托最优解，并比较了最佳头部形状和原始头部形状。混合代理模型和CMOPSO算法的使用大大提高了优化效率。选择CMOPSO算法作为优化算法。优化后，获得了一系列帕累托最优解，并比较了最佳头部形状和原始头部形状。混合代理模型和CMOPSO算法的使用大大提高了优化效率。