IPAC2018 - List of Authors (Li, P.)

Paper	Title	Page
TUPMF045	Performance Optimization of a Prototype Undulator U38 Using Multi-Objective Genetic Algorithm	1353
	L.G. Yan, D.R. Deng, P. Li, D. Wu CAEP/IAE, Mianyang, Sichuan, People's Republic of China
	Funding: The project of the national large-scale instrument development: 2011YQ130018; National Natural Science Foundation of China: 11505174, 11505173 and 11605190. Genetic Algorithm (GA) is one of the most excellent method to search the optimal solution of a problem, which has been applied to solve various problems. It is hard to estimate shim applied on raw undulator precisely. There are many methods have been developed to solve the problem. In this proceeding, we measured the magnetic field distribution of prototype undulator U38 and concluded the shim using multi-objective GA. The code was written with the language of Python and based on the package pyevolve. A multi-objective fitness function was setup to implement the multi-objective optimization. Experimentally，performances satisfied the requirements by shimming U38 three times. The trajectory center deviation, peak-to-peak error and phase error are reduced to 0.15 mm, 0.49% and 1°.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF045
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Paper

Title

Page

Performance Optimization of a Prototype Undulator U38 Using Multi-Objective Genetic Algorithm

1353

L.G. Yan, D.R. Deng, P. Li, D. Wu
CAEP/IAE, Mianyang, Sichuan, People's Republic of China

Funding: The project of the national large-scale instrument development: 2011YQ130018; National Natural Science Foundation of China: 11505174, 11505173 and 11605190.
Genetic Algorithm (GA) is one of the most excellent method to search the optimal solution of a problem, which has been applied to solve various problems. It is hard to estimate shim applied on raw undulator precisely. There are many methods have been developed to solve the problem. In this proceeding, we measured the magnetic field distribution of prototype undulator U38 and concluded the shim using multi-objective GA. The code was written with the language of Python and based on the package pyevolve. A multi-objective fitness function was setup to implement the multi-objective optimization. Experimentally，performances satisfied the requirements by shimming U38 three times. The trajectory center deviation, peak-to-peak error and phase error are reduced to 0.15 mm, 0.49% and 1°.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF045

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)