Abstract The generalized effective global optimization (EGO) method based on Kriging model can sequentially solve the expensive black-box problems. However, it can only obtain one sampling point in a cycle, which will result in more time spent on expensive function evaluations and affect the global convergence. To this end, A Kriging-based adaptive global optimization method with generalized expected improvement (KAGO-GEI) is proposed. It divides the enhanced generalized expected improvement (GEI) criterion which recursively changes in the iterative process into double objectives, and then uses multi-objective PSO method to optimize the two objectives to produce the Pareto frontier. Further, more valuable sampling points from Pareto frontier are screened and corrected as the expensive-evaluation points for updating Kriging model. Test results on eighteen benchmark functions and crop evapotranspiration calculation example show that the proposed method is superior to other classical optimization methods in terms of convergence and accuracy in most cases.

中文翻译：

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一种具有广义预期改进的基于克里金的自适应全局优化方法及其在数值模拟和作物蒸散中的应用

摘要 基于克里金模型的广义有效全局优化（EGO）方法可以顺序解决代价高昂的黑盒问题。然而，它在一个循环中只能获得一个采样点，这将导致更多的时间花费在昂贵的函数评估上并影响全局收敛。为此，提出了一种具有广义期望改进的基于克里金法的自适应全局优化方法（KAGO-GEI）。它将迭代过程中递归变化的增强广义期望改进（GEI）准则划分为双目标，然后使用多目标PSO方法对这两个目标进行优化，产生帕累托前沿。此外，来自帕累托边界的更有价值的采样点被筛选和校正为更新克里金模型的昂贵评估点。