Kriging is a prevalent surrogate model technique in optimization and data-driven analysis, known for its high accuracy and statistical error estimation. However, training Kriging models often requires extensive global optimization of hyperparameters, posing significant challenges when applying these methods to large-scale datasets. Previous research has mainly focused on expediting the training process by reducing the dimensions of design variables and hyperparameters, but this approach frequently results in information loss that adversely affects model accuracy. To address this issue, we introduce a novel methodology called KBCC (Kriging utilizing Boxplot and Correlation Coefficient), which: 1) extracts trends and characteristics of the dataset using boxplot parameters and correlation coefficients; 2) quickly estimates the hyperparameters via a pre-trained surrogate model; and 3) finalizes the hyperparameters with a two-step local optimization. In constructing the pre-trained hyperparameter predictor, we utilize datasets from various functions to enhance its prediction capability. This procedure allows for efficient search of optimal hyperparameter combinations without the need for dimension reduction, thereby accelerating Kriging training across diverse datasets while ensuring high accuracy. The performance and efficiency of KBCC are validated through six numerical test problems and three engineering examples, demonstrating its superior performance relative to conventional methodologies.

中文翻译：

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利用箱线图和相关系数进行大规模数据的代理辅助克里金训练


克里金法是优化和数据驱动分析中一种流行的代理模型技术，以其高精度和统计误差估计而闻名。但是，训练 Kriging 模型通常需要对超参数进行广泛的全局优化，因此在将这些方法应用于大规模数据集时会带来重大挑战。以前的研究主要集中在通过减少设计变量和超参数的维度来加快训练过程，但这种方法经常导致信息丢失，从而对模型准确性产生不利影响。为了解决这个问题，我们引入了一种称为 KBCC（利用箱线图和相关系数的克里金法）的新方法，该方法：1） 使用箱线图参数和相关系数提取数据集的趋势和特征;2） 通过预先训练的代理模型快速估计超参数;3） 通过两步局部优化完成超参数。在构建预训练的超参数预测器时，我们利用来自各种函数的数据集来增强其预测能力。此过程允许在不需要降维的情况下高效搜索最佳超参数组合，从而加速跨不同数据集的克里金训练，同时确保高准确性。KBCC 的性能和效率通过 6 个数值测试问题和 3 个工程示例得到验证，证明了其相对于传统方法的卓越性能。