2020
DOI: 10.3390/computers9020032
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An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

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 CC… Show more

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Cited by 4 publications
(6 citation statements)
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“…Therefore, in order to solve CCP in (10) efficiently, a new optimization method called Adaptive DE with Pruning technique (ADEP) is proposed. In the proposed ADEP, three techniques are integrated into the original DE: 1) Adaptive control of parameters [21]; 2) Constraint handling based on feasibility rule [22]; and 3) Pruning technique in selection [14].…”
Section: Optimization Methodsmentioning
confidence: 99%
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“…Therefore, in order to solve CCP in (10) efficiently, a new optimization method called Adaptive DE with Pruning technique (ADEP) is proposed. In the proposed ADEP, three techniques are integrated into the original DE: 1) Adaptive control of parameters [21]; 2) Constraint handling based on feasibility rule [22]; and 3) Pruning technique in selection [14].…”
Section: Optimization Methodsmentioning
confidence: 99%
“…Therefore, the number of data ξ ∈ B is too large to evaluate the empirical probability in (4). For solving CCP in ( 5) practically, we have to neglect the most of data [14].…”
Section: Formulation Of Data-driven Ccpmentioning
confidence: 99%
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