论文标题
通过基于顺序模型的优化学习对水分配网络的最佳控制
Learning Optimal Control of Water Distribution Networks through Sequential Model-based Optimization
论文作者
论文摘要
基于顺序模型的贝叶斯优化已成功地应用于几个应用程序域，其特征是复杂的搜索空间，例如自动化的机器学习和神经体系结构搜索。本文着重于最佳控制问题，提出了一个基于模型的贝叶斯优化框架，以学习最佳控制策略。提供了对问题的一般形式化，以及与城市水发出网络中泵送操作的优化有关的特定实例。报告了现实生活中的水分配网络上的相关结果，并比较了所提出的框架的不同可能选择。
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.
