Approximate Solution using Machine Learning and Evolutionary Algorithm for Large-Scale Data-Driven Chance Constrained Problems
Approximate Solution using Machine Learning and Evolutionary Algorithm for Large-Scale Data-Driven Chance Constrained Problems
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DOI：
10.1109/scisisis55246.2022.10002127
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发表时间：
2022-11
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
通过使用大规模数据集，机会约束问题（CCP）被表述为确定性优化问题，称为确定性数据驱动CCP （DDCP）。DDCP不仅通过解决方案是否满足指定充分性水平上的约束来确定解决方案的可行性，而且还将所有给定的数据分为两类。然后将支持向量机（SVM）和进化算法（EA）相结合，提出了一种近似求解DDCP的方法。为了选择支持向量机的训练集，采用了主成分分析的分层抽样方法。最后，通过一个实际应用，即用DDCP表示的基站定位问题，对所提出的近似求解方法的性能进行了评价。
By using a large-scale data set, Chance-Constraint Problem (CCP) is formulated as a deterministic optimization problem called Deterministic Data-driven CCP (DDCP). DDCP not only determines the feasibility of a solution by whether or not it satisfies the constraints at a specified sufficiency level, but also classifies all given data into two classes. Then an approximate solution method for DDCP is proposed by combining a Support Vector Machine (SVM) and an Evolutionary Algorithm (EA). In order to select a training set for the SVM, a stratified sampling method using principal component analysis is employed. Finally, the performance of the proposed approximate solution method is evaluated through a real-world application, namely, the base-station location problem which is formulated as DDCP.