Keyword: dynamic-aperture
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SAPAF01 Genetic Algorithm Enhanced by Machine Learning in Dynamic Aperture Optimization lattice, sextupole, storage-ring, resonance 8
  • Y. Li, W.X. Cheng, R.S. Rainer, L. Yu
    BNL, Upton, Long Island, New York, USA
  Funding: This work was supported by Department of Energy Contract No. DE-SC0012704
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 NSLS-II Ring. During the evolution employed by the genetic algorithm, the population is classified into different clusters. The clusters with top average fitness are given elite status. Intervention is implemented by repopulating some potentially competitive candidates based on the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is improved while diversity is not lost. 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. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
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About • paper received ※ 15 October 2018       paper accepted ※ 24 October 2018       issue date ※ 26 January 2019  
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