Genetic algorithm enhanced by machine learning in dynamic aperture optimization
Abstract
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
- Authors:
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1439379
- Alternate Identifier(s):
- OSTI ID: 1439451
- Report Number(s):
- BNL-205720-2018-JAAM
Journal ID: ISSN 2469-9888; PRABCJ; 054601
- Grant/Contract Number:
- SC0012704
- Resource Type:
- Published Article
- Journal Name:
- Physical Review Accelerators and Beams
- Additional Journal Information:
- Journal Name: Physical Review Accelerators and Beams Journal Volume: 21 Journal Issue: 5; Journal ID: ISSN 2469-9888
- Publisher:
- American Physical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 43 PARTICLE ACCELERATORS
Citation Formats
Li, Yongjun, Cheng, Weixing, Yu, Li Hua, and Rainer, Robert. Genetic algorithm enhanced by machine learning in dynamic aperture optimization. United States: N. p., 2018.
Web. doi:10.1103/PhysRevAccelBeams.21.054601.
Li, Yongjun, Cheng, Weixing, Yu, Li Hua, & Rainer, Robert. Genetic algorithm enhanced by machine learning in dynamic aperture optimization. United States. https://doi.org/10.1103/PhysRevAccelBeams.21.054601
Li, Yongjun, Cheng, Weixing, Yu, Li Hua, and Rainer, Robert. Tue .
"Genetic algorithm enhanced by machine learning in dynamic aperture optimization". United States. https://doi.org/10.1103/PhysRevAccelBeams.21.054601.
@article{osti_1439379,
title = {Genetic algorithm enhanced by machine learning in dynamic aperture optimization},
author = {Li, Yongjun and Cheng, Weixing and Yu, Li Hua and Rainer, Robert},
abstractNote = {With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.},
doi = {10.1103/PhysRevAccelBeams.21.054601},
journal = {Physical Review Accelerators and Beams},
number = 5,
volume = 21,
place = {United States},
year = {Tue May 29 00:00:00 EDT 2018},
month = {Tue May 29 00:00:00 EDT 2018}
}
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https://doi.org/10.1103/PhysRevAccelBeams.21.054601
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