Online optimization is critical for realizing the design performance of accelerators. Highly efficient stochastic optimization algorithms are needed for many online accelerator optimization problems in order to find the global optimum in the non-linear, coupled parameter space. In this study, we propose to use the multi-generation Gaussian process optimizer for online accelerator optimization and demonstrate that the algorithm is significantly more efficient than other stochastic algorithms that are commonly used in the accelerator community.

中文翻译：

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基于基于机器学习的随机算法的在线加速器优化

在线优化对于实现加速器的设计性能至关重要。许多在线加速器优化问题都需要高效的随机优化算法，以便在非线性耦合参数空间中找到全局最优值。在这项研究中，我们建议使用多代高斯过程优化器进行在线加速器优化，并证明该算法比加速器社区中常用的其他随机算法效率更高。