Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade
Xiaobiao Huang,James Safranek +1 more
TL;DR: The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population, which may make it more suitable for many accelerator optimization applications.
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Abstract: Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
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Citations
Modification and optimization of the storage ring lattice of the High Energy Photon Source
Yi Jiao,Fu-San Chen,Ping He,Chunhua Li,Jingyi Li,Qing Qin,Huamin Qu,Jinyu Wan,Jiuqing Wang,Gang Xu +9 more
- 17 Jul 2020
TL;DR: The background and reasons for the modifications are introduced and the linear optics and simulation results for the nonlinear performance of the modified lattice of the HEPS storage ring are presented.
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Optimizing the lattice design for a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms
TL;DR: In this paper, a diffraction-limited storage ring (DLSR) consisting of compact multi-bend achromats (MBAs) is designed to achieve an ultralow emittance and a satisfactory nonlinear performance, due to extremely large nonlinearities and limited tuning ranges of the element parameters.
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A Genetic Algorithm for Chromaticity Correction in Diffraction Limited Storage Rings
TL;DR: In this paper, a multi-objective genetic algorithm is developed for optimizing nonlinearities in diffraction limited storage rings, which makes use of dominance constraints to breed desirable properties into the early generations.
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Bio-inspired and artificial intelligence enabled hydro-economic model for diversified agricultural management
TL;DR: In this article , the authors proposed a combinatorial optimization approach for land allocation considering agronomic, socioeconomic, environmental and hydro-climatic objectives using bio-inspired optimization algorithms.
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Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms*
TL;DR: In this article, the potential of a diffraction-limited storage ring (DLSR) design can be explored with a successive and iterative implementation of the multiobjective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA).
24
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