Nuclear Techniques ›› 2016, Vol. 39 ›› Issue (2): 20101-020101.doi: 10.11889/j.0253-3219.2016.hjs.39.020101

• SYNCHROTRON RADIATION TECHNOLOGY AND APPLICATIONS •     Next Articles

Application of genetic algorithm for optimization design of free electron laser

ZHANG Baixin1,2, ZHANG Tong1, CHEN Jianhui1, LIU Bo1, WANG Dong1   

  1. 1 Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading Campus, Shanghai 201800, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2015-12-01 Revised:2015-12-30 Online:2016-02-10 Published:2016-02-17
  • Supported by:

    Supported by the National Natural Science Foundation of China (No.11175241) First author: ZHANG Baixin, male, born in 1991, graduated from Nanjing University of Aeronautics & Astronautics in 2013, master student, focusing on the free electron laser upper application software development

Abstract:

Background: Optimization of free-electron laser (FEL) facilities is of great significance to achieve radiations of high-quality, e.g. higher brilliance, purer spectrum. In addition, these properties are affected by various parameters, e.g. the electron trajectory along the accelerator and undulator, the beam envelope or beta function, which could be changed by tuning the correctors and quadrupoles. Purpose: This study aims to find a set of suitable parameters to optimize the radiation power. Methods: The genetic algorithm (GA) is applied to investigation of the FEL power optimization with respect to the focus-defocus-focus (FODO) lattice configuration between the undulator segmentations. A friendly graphical user interface (GUI) is designed for deployment of the software. Results: The preliminary study of the machine optimization shows that it is efficient to find good pair of FODO lattice to get power high enough even if it is not globally optimized. Conclusion: This application of genetic algorithm for optimization design of free electron laser is efficient and stable based on many experiments of multi-variable optimization problems, and it is helpful to the future application to the genuine FEL machine optimization.

Key words: Real-coded, Genetic algorithm, Free-electron laser

CLC Number: 

  • TL506