A Sequential Optimization Method Based on Kriging Surrogate Model

机译：基于克里格代理模型的顺序优化方法

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A multi-point sampling criterion considering the predictor and its uncertainty simultaneously is proposed based on kriging surrogate model, and a sequential approximation optimization method is developed. Multi-point sampling criterion is used to select the new samples by considering the distributions of the initial samples and the characteristics of the predicted target function. The proposed method selects more than one new sample for each optimization iteration, thus it can be performed by parallel computation or multi-computer runs which improve effectively the computational efficiency. Take tow typical mathematical functions as examples, the proposed method is compared with expected improvement criterion method and the results show the proposed method can effectively search the global optimum.

机译：提出了一种基于克里格代理模型的同时考虑预测因子及其不确定性的多点采样准则，并提出了一种逐次逼近优化方法。多点采样标准用于通过考虑初始样本的分布和预测目标函数的特征来选择新样本。所提出的方法为每个优化迭代选择一个以上的新样本，因此可以通过并行计算或多计算机运行来执行，从而有效地提高了计算效率。以两个典型的数学函数为例，将该方法与期望改进准则方法进行了比较，结果表明该方法可以有效地寻找全局最优值。

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《》|2011年|p.232-235|共4页
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作者
Gao Yuehua; Wang Xicheng; Wang Yuedong; Li Yonghua;
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正文语种
中图分类计算技术、计算机技术;
关键词
kriging; sampling criterion; sequential optimization; surrogate model;

机译：克里格抽样准则顺序优化替代模型;

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A Sequential Optimization Method Based on Kriging Surrogate Model

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