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An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

Department of Informatics, Kindai University, Osaka 577-8502, Japan
This paper is an extended version of our paper published in the 25th International Conference on Information and Software Technologies (ICIST 2019), held in Kaunas University of Technology (Lithuania) on 10–12 October 2019.
Computers 2020, 9(2), 32; https://doi.org/10.3390/computers9020032
Received: 19 March 2020 / Revised: 13 April 2020 / Accepted: 14 April 2020 / Published: 16 April 2020
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. View Full-Text
Keywords: chance constrained problem; data reduction; differential evolution; big data chance constrained problem; data reduction; differential evolution; big data
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Tagawa, K. An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution. Computers 2020, 9, 32.

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