Online accelerator optimization with a machine learning-based stochastic algorithm. Issue 1 (24th December 2020)

• Record Type: Journal Article
• Authors: Zhang, Zhe; Song, Minghao; Huang, Xiaobiao
• Is Part Of: Machine learning: science and technology. Volume 2:Issue 1(2021)
• Publication Date: 2020-12-24
• Subjects: online optimization -- Gaussian process -- evolutionary algorithm; 006.31
• DOI: 10.1088/2632-2153/abc81e
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: 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.