Considering the high computational cost of the optimisation process of complex transportation equipment, an efficient structural optimisation method for transportation equipment based on the multi-criteria sample updating and management strategy of the approximation model is proposed. First, the approximation models of the objective function and constraints are established based on the radial basis function, and the intergeneration projection genetic algorithm is applied to find the optimal solution. According to error evaluation of the optimal solution, second, the function fluctuation index is used to adopt the local sample points. Meanwhile, the global sample points are solved by the inherited Latin hypercube design to ensure the distribution uniformity of samples in the whole design space. Furthermore, the weighted Euclidean distance criterion is applied to evaluate the rationality between the samples. Then, the qualified samples are added to the sample space to update the approximation models of the transportation equipment. Based on the above multi-criteria sample updating and management strategy, more accurate optimisation results could be obtained. Finally, the effectiveness of the method and its applicability in practical application of transportation equipment are investigated by one numerical test and one engineering example.

中文翻译：

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使用自适应近似模型对运输设备进行结构优化

针对复杂交通运输设备优化过程计算成本高的问题，提出了一种基于多准则样本更新和近似模型管理策略的交通运输设备结构高效优化方法。首先，基于径向基函数建立目标函数和约束的逼近模型，并应用代际投影遗传算法寻找最优解。根据最优解的误差评价，其次，采用函数波动指标采用局部样本点。同时，全局样本点采用继承的拉丁超立方设计求解，保证样本在整个设计空间的分布均匀性。此外，加权欧几里得距离准则被应用于评估样本之间的合理性。然后，将合格样本加入样本空间，更新运输设备的近似模型。基于上述多准则样本更新和管理策略，可以获得更准确的优化结果。最后通过数值试验和工程实例验证了该方法的有效性及其在运输设备实际应用中的适用性。