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@inproceedings{shang:ipac2021-tupab286,
author = {H. Shang and M. Borland and X. Huang and M. Song and Y. Sun and Z. Zhang},
title = {{Experience with On-line Optimizers for APS Linac Front End Optimization}},
booktitle = {Proc. IPAC'21},
pages = {2151--2154},
eid = {TUPAB286},
language = {english},
keywords = {linac, gun, operation, controls, injection},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-TUPAB286},
url = {https://jacow.org/ipac2021/papers/tupab286.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-TUPAB286},
abstract = {{While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front end beam dynamics has been difficult to model. One of the issues is that beam properties from the thermionic gun can vary from time to time. As a result, linac front end beam tuning is required to establish good matching and maximize the charge transported through the linac. We have been using a traditional simplex optimizer to find the best settings for the gun front end magnets and steering magnets. However, it takes a long time and requires some fair initial conditions. Therefore, we imported other on-line optimizers, such as robust conjugate direction search (RCDS) which is a classic optimizer as simplex, multi-objective particle swarm (MOPSO), and multi-generation gaussian process optimizer (MG-GPO) which is based on machine learning technique. In this paper we report our experience with these on-line optimizers for maximum bunch charge transportation efficiency through the linac.}},
}