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https://doi.org/10.18429/JACoW-IPAC2021-MOPAB106
Title Enhancing the MOGA Optimization Process at ALS-U with Machine Learning
Authors
  • Y. Lu, M.P. Ehrlichman, T. Hellert, S.C. Leemann, H. Nishimura, C. Sun, M. Venturini
    LBNL, Berkeley, California, USA
Abstract The bare lattice optimization for the linear and nonlinear ALS-U storage ring lattice, even without reverse bending, comprises 11 degrees of freedom (DoF) and is therefore a very complex and highly time-consuming process. This design process relies heavily on multi-objective genetic algorithms (MOGA), usually requiring many months of experienced scientists’ time. The main problem lies in having to evaluate numbers of candidate lattices due to the stochastic process of MOGA. Although almost all of these candidates are eventually rejected, they nevertheless require extensive particle tracking to arrive at a Pareto front. We therefore propose a novel Machine Learning (ML) pipeline that nonlinear tracking is replaced by two well-trained neural networks (NNs) to predict dynamic aperture (DA) and momentum aperture (MA) for any lattice candidate. Initial training of these models takes only several minutes on conventional CPUs while predictions are then rendered near instantaneously. We present this novel method and demonstrate the resulting orders of magnitude speedup of the ML-enhanced MOGA process on a 2-DoF problem as well as first results on a more complex 11-DoF problem.
Funding This research is funded by the US Department of Energy(BES & ASCR Programs), and supported by the Director of the Office of Science of the US Department of Energy under Contract No. DEAC02-05CH11231.
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Conference IPAC2021
Series International Particle Accelerator Conference (12th)
Location Campinas, SP, Brazil
Date 24-28 May 2021
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-214-1
Online ISSN 2673-5490
Received 19 May 2021
Accepted 01 June 2021
Issue Date 18 August 2021
DOI doi:10.18429/JACoW-IPAC2021-MOPAB106
Pages 387-390
Copyright
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.