Data Reduction via Stratified Sampling for Chance Constrained Optimization with Application to Flood Control Planning
Data Reduction via Stratified Sampling for Chance Constrained Optimization with Application to Flood Control Planning
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DOI：
10.1007/978-3-030-30275-7_38
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
K. Tagawa
中科院分区：
文献类型：
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作者：
K. Tagawa
由于先进的信息技术，大量的数据可用于科普机会约束问题（CCP）。在本文中，通过使用这样一个庞大的数据集，CCP的松弛问题制定。然后提出了一种新的基于分层抽样的数据约简方法来处理实际的海量数据集。为了有效地解决松弛问题，本文还提出了一种样本节省技术，采用自适应差分进化算法。最后，将该方法应用于编制的防洪规划CCP。
Due to advanced information technologies, huge data are available to cope with Chance Constrained Problems (CCPs). In this paper, a relaxation problem of CCP is formulated by using such a huge data set. Then a new data reduction method based on stratified sampling is proposed to deal with the huge data set practically. A sample saving technique is also proposed to solve the relaxation problem efficiently by using an adaptive differential evolution algorithm. Finally, the proposed method is applied to the flood control planning formulated as CCP.