Abstract
The article discusses the application of the genetic global search algorithm to the problem of beam dynamics optimization. The algorithm uses normal distribution to form new generations and provides covariance matrix adaptation during random search. The method is easy to use because it does not require calculation of the covariance matrix. The algorithm application is illustrated in the problem of global extremum search for the functional that characterizes beam dynamics quality in a linear accelerator. The extremal problem under study has a large number of variables; the objective function is multi-extreme. Therefore, the use of the stochastic method is the preferred way to achieve the goal. The algorithm quickly converges and can be successfully used in solving multidimensional optimization problems, including its combination with directed methods. The optimization results are presented and discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ermakov, S.M., Mitioglova, L.V.: On extreme search method based on the estimation of the covariance matrix. Autom. Comput. Eng. 5, 38–41 (1977). (in Russian)
Vladimirova, L.V., Ermakov, S.M.: Random search method with a “Memory” for global extremum of a function. In: Proceedings of 10th International Workshop on Simulation and Statistics. Universitat Salzburg, Salzburg, Workshop booklet, 89, (2019). https://datascience.sbg.ac.at/SimStatSalzburg2019/SimStat2019BoA.pdf
Igel, C., Hansen, N., Roth, S.: Covariance matrix adaptation for multi-objective optimization. Evol. Comput. 15(l), l–28 (2007)
Ermakov, S.M., Semenchikov, D.N.: Genetic global optimization algorithms. In: Communications in Statistics, Part B: Simulation and Computation (2019). https://doi.org/10.1080/03610918.2019.1672739
Ovsyannikov, A.D.: Mathematical models of beam dynamics optimization. VVM, St. Petersburg, p. 181 (2014) (in Russian)
Ovsyannikov, D.A., Ovsyannikov, A.D., Vorogushin, M.F., Svistunov, Yu.A., Durkin, A.P.: Beam dynamics optimization: Models, methods and applications. Nucl. Instr. Meth. Phys. Res. Sect. A 558(1), 11–19 (2006)
Ovsyannikov, A.D., Shirokolobov, A.Y.: Mathematical model of beam dynamics optimization in traveling wave. In: Proceedings of RuPAC-2012. JACoW, pp. 355–357 (2012). http://www.JACoW.org
Kennedy, J.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks IV, pp. 1942–1948 (1995)
Altsybeev, V.V., Svistunov, Yu.A., Durkin, A.P., Ovsyannikov, D.A.: Preacceleration of the multicharged ions with the different A/Z ratios in single RFQ channel. Cybern. Phys. 7(2), 49–56 (2018)
Rubtsova, I.D., Vladimirova, L.V., Edamenko, N.S., Goncharova, A.B.: Intense beam dynamics study in Alvarez accelerator. Phys. At. Nucl. 82, 1527–1531 (2019). https://link.springer.com/article/10.1134/S106377881911019X
Zhdanova, A.Y., Rubtsova, I.D.: Modeling and optimization of intense beam dynamics in traveling-wave field. In: Proceedings of V International Conference on Laser & Plasma Researches and Technologies (LaPlas-2019), part 2, Moscow, National Research Nuclear University MEPhI, pp. 160–161 (2019)
Bartolini, R., Apollonio, M., Martin, I.P.S.: Multiobjective genetic algorithm optimization of the beam dynamics in linac drivers for free electron lasers. Phys. Rev. ST Accel. Beams 15(3), 030701 (2012)
Vladimirova, L.V.: Multicriterial approach to beam dynamics optimization problem. J. Phys. Conf. Ser. 747(1), 012070 (2016)
Balabanov, M.Yu.: On initial control choice in charged particles beams dynamic optimization problems. Vestnik of Saint Petersburg University. Appl. Math. Comput. Sci. Control Process. 3, 93–99 (2010)
Gao, W., Wang, L., Li, W.: Simultaneous optimization of beam emittance and dynamic aperture for electron storage ring using genetic algorithm. Phys. Rev. ST Accel. Beams 14(9), 094001 (2011)
Vladimirova, L.V., Zhdanova, A.Y., Rubtsova, I.D.: Application of the genetic global search algorithm in beam dynamics optimization problem. In: Proceedings of VI International Conference on Laser & Plasma researches and technologies (LaPlas-2020), part 1, Moscow, National Research Nuclear University MEPhI, pp. 91–92 (2020)
Acknowledgements
The authors are grateful to Professor S.M. Ermakov for his attention to the work and valuable comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vladimirova, L., Zhdanova, A., Rubtsova, I., Edamenko, N. (2022). Genetic Stochastic Algorithm Application in Beam Dynamics Optimization Problem. In: Smirnov, N., Golovkina, A. (eds) Stability and Control Processes. SCP 2020. Lecture Notes in Control and Information Sciences - Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-87966-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-87966-2_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87965-5
Online ISBN: 978-3-030-87966-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)