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• W3017102709 abstract "In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP." @default.
• W3017102709 created "2020-04-24" @default.
• W3017102709 creator A5022853937 @default.
• W3017102709 date "2020-04-16" @default.
• W3017102709 modified "2023-09-25" @default.
• W3017102709 title "An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution" @default.
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• W3017102709 doi "https://doi.org/10.3390/computers9020032" @default.
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