基于序贯模型优化的给水管网学习最优控制
Learning Optimal Control of Water Distribution Networks through Sequential Model-based Optimization
摘要：
基于序贯模型的贝叶斯优化算法已经成功地应用于自动机器学习和神经结构搜索等具有复杂搜索空间的应用领域。针对最优控制问题，提出了一种基于序贯模型的贝叶斯优化框架来学习最优控制策略。文中给出了问题的一般形式化描述，并给出了城市供水管网抽水优化问题的具体实例。现实生活中的配水网络的相关结果报告，比较不同的可能的选择，建议的框架。
英文摘要：
Sequential Model-based Bayesian Optimization has been successful-ly applied to several application domains, characterized by complex search spaces, such as Automated Machine Learning and Neural Architecture Search. This paper focuses on optimal control problems, proposing a Sequential Model-based Bayesian Optimization framework to learn optimal control strategies. A quite general formalization of the problem is provided, along with a specific instance related to optimization of pumping operations in an urban Water Distri-bution Network. Relevant results on a real-life Water Distribution Network are reported, comparing different possible choices for the proposed framework.
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