用最优传输理论改进风场布局优化，更快更准。
Optimal Transport-based Permutation-Invariant Bayesian Optimization of Offshore Wind Farm Layouts
- 基于最优传输设计排列不变的贝叶斯优化方法
- 相比传统方法提升风场布局效率，计算时间减半
- 适合解决风电场等具有对称性的布局优化问题
贝叶斯优化（BO）广泛应用于目标函数昂贵、黑箱且非凸的优化问题。然而，原始的BO算法无法利用目标问题可能存在的对称性。一个典型例子是布局优化问题：决策变量为连续空间中的有限点集，点的顺序不影响目标函数值。我们称此为布局优化，区别于点云优化（顺序影响结果）。本文以海上风电场布局优化为例，考虑相同风力涡轮机的配置问题——任意两台涡轮机互换位置不会影响年发电量。基于最优传输理论，提出一种排列不变的贝叶斯优化方法（PIBO），实验证明其在生成更优风场布局的同时，计算时间约减少一半。
原文摘要 · Abstract (English)
Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-convex objective function. However, the vanilla BO algorithm is not able to exploit possible symmetries characterizing the target problem. An intuitive case is given by optimal location problems, whose decision variables refer to a finite set of points within a continuous space, with the order of points not affecting the value of the objective function. We refer to this setting as optimization over layouts to distinguish from optimization over point-clouds where, instead, the order of points counts. As an instance of optimization over layouts we consider a real-life industrial-relevant application, that is the optimization of the layout of an offshore wind farm: given identical wind turbines, switching any pair of them has not any effect on the annual energy production. Based on Optimal Transport theory, we propose a Permutation-Invariant BO approach, namely PIBO, proved to provide better wind farm layouts when compared to the vanilla BO approach while cutting computation time roughly in half.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。