Dynamic aperture optimization with diffusion map analysis at CEPC using differential evolution algorithm

Wu, Jin; Zhang, Yuan; Qin, Qing; Wang, Yiwei; Chen, Yu; Zhou, Demin

doi:10.1016/j.nima.2020.163517

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 20) multiple objectives have to be optimized simultaneously. There are many multiobjective algorithms for this purpose, such as particle swarm optimization [6,[19][20][21], ant colony optimization [22], simulated annealing [23], artificial immune system [24], differential evolution [7,8] or genetic algorithm [25]. Multiobjective genetic algorithms (MOGA) are probably the most popular and they have already been successfully applied in the field of particle accelerator physics [12,18,[26][27][28][29], in particular also for the DA optimization [3,4,10,11,13,14].…”

Section: Multiobjective Genetic Algorithm (Moga)mentioning

confidence: 99%

“…Out of the many multiobjective optimization algorithms, particle swarm optimization [6], differential evolution [7,8] and multiobjective genetic algorithms [3,[9][10][11][12][13][14] have already been successfully applied to the problem of optimizing the DA. In this work a multiobjective genetic algorithm (MOGA) is chosen and further extended * marija.kranjcevic@psi.ch with constraint-handling methods (section III).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks

Kranjčević

Riemann

Adelmann

et al. 2021

Phys. Rev. Accel. Beams

View full text Add to dashboard Cite

Modern synchrotron light source storage rings, such as the Swiss Light Source upgrade (SLS 2.0), use multi-bend achromats in their arc segments to achieve unprecedented brilliance. This performance comes at the cost of increased focusing requirements, which in turn require stronger sextupole and higher-order multipole fields for compensation and lead to a considerable decrease in the dynamic aperture and/or energy acceptance. In this paper, to increase these two quantities, a multi-objective genetic algorithm (MOGA) is combined with a modified version of the well-known tracking code tracy. As a first approach, a massively parallel implementation of a MOGA is used. Compared to a manually obtained solution this approach yields very good results. However, it requires a long computation time. As a second approach, a surrogate model based on artificial neural networks is used in the optimization. This improves the computation time, but the results quality deteriorates. As a third approach, the surrogate model is re-trained during the optimization. This ensures a solution quality comparable to the one obtained with the first approach while also providing an order of magnitude speedup. Finally, good candidate solutions for SLS 2.0 are shown and further analyzed.

show abstract

Section: Multiobjective Genetic Algorithm (Moga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks

Kranjčević

Riemann

Adelmann

et al. 2021

Phys. Rev. Accel. Beams

View full text Add to dashboard Cite

show abstract

Historical Elite Differential Evolution Based on Particle Swarm Optimization Algorithm for Texture Optimization with Application in Particle Physics

Martínez-Guerrero,

Lagos-Eulogio,

Miranda-Romagnoli

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called “HE-DEPSO” to obtain chi-squared values that are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model.

show abstract

Dynamic aperture optimization with diffusion map analysis at CEPC using differential evolution algorithm

Cited by 2 publications

References 10 publications

Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks

Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks

Historical Elite Differential Evolution Based on Particle Swarm Optimization Algorithm for Texture Optimization with Application in Particle Physics

Contact Info

Product

Resources

About