Multiobjective optimization of dynamic aperture

Yang, Lingyun; Li, Yongjun; Guo, Weiming; Krinsky, S.

doi:10.1103/physrevstab.14.054001

Cited by 53 publications

(36 citation statements)

References 13 publications

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“…GAs were originally introduced to particle accelerator related research for a wiggler design in the early 1990s [6]. Thereafter, they have been successfully applied in more areas such as designing injector systems [7], diagnosing and designing accelerating cavities [8][9][10][11][12], and optimizing beam optics design in storage rings [13][14][15]. Some of these applications of GAs in the particle accelerator community are well summarized in Ref.…”

Section: Introductionmentioning

confidence: 99%

Machine based optimization using genetic algorithms in a storage ring

Tian

Safranek

Yan

2014

Phys. Rev. ST Accel. Beams

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The genetic algorithm (GA) has been a popular technique in optimizing the design of particle accelerators. As a population based algorithm, GA requires a large number of evaluations of the objective functions, which can be time consuming. One can benefit from parallel computing with significantly reduced computing time when fulfilling the function evaluation by a numerical machine model in simulation codes. Indeed, this is the most common approach in GA applications. In this paper, instead of applying GA in the conventional numerical calculations as described above, we present a successful experimental demonstration of implementing GA in real machine based optimization. We conduct the minimization of the average vertical beam size of the SPEAR3 storage ring using GA. Beam loss rate is chosen as the sole objective function because it is inversely proportional to the vertical beam size and can be measured instantaneously in SPEAR3. The decision variables are the strengths of SPEAR3 skew quadrupoles, by varying which we can change both the betatron coupling and the vertical dispersion while searching for the minimum beam size. The results in this paper can shed light on new applications of GAs in the particle accelerator community, for example, optimizing the luminosity of a high energy collider or the injection efficiency of a diffraction limited storage ring in real time.

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Section: Introductionmentioning

confidence: 99%

Machine based optimization using genetic algorithms in a storage ring

Tian

Safranek

Yan

2014

Phys. Rev. ST Accel. Beams

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“…[10]). However, if a more thorough search of the parameter space is desired in order to gain more assurance that the global minimum is reached, stochastic optimization algorithms such as genetic algorithms [4,5,11,12] and particle swarm optimization (PSO) algorithms [6,13,14] can be used. Studies have indicated the particle swarm method is a few times more efficient than a widely used genetic algorithm because solutions evaluated by the former are considerably more diverse than that of the latter.…”

Section: Considerations Of Online Optimization Of Nonlinear Beam mentioning

confidence: 99%

“…In recent years advanced multiobjective optimization methods have been widely used for the optimization of nonlinear beam dynamics in storage ring lattice design [4][5][6], which in simulation can significantly improve the dynamic and momentum apertures. However, given the potential differences between the real machine and the lattice model, it is likely that the optimal condition as determined by the model optimization will not give the expected result in the machine.…”

Section: Introductionmentioning

confidence: 99%

Online optimization of storage ring nonlinear beam dynamics

Huang

Safranek

2015

Phys. Rev. ST Accel. Beams

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We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.

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“…In this paper an available framework of an evolutionary algorithm is applied that is complemented with a local search. Several papers such as [4][5][6][7][8][9][10] already showed the successful application of evolutionary algorithms like particle swarm, differential evolution or NSGA-II [11] in connection with particle accelerator modelling.…”

Section: Introductionmentioning

confidence: 99%

Matching of turn pattern measurements for cyclotrons using multiobjective optimization

Frey

Snuverink

Baumgarten

et al. 2019

Phys. Rev. Accel. Beams

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The usage of numerical models to study the evolution of particle beams is an essential step in the design process of particle accelerators. However, uncertainties of input quantities such as beam energy and magnetic field lead to simulation results that do not fully agree with measurements, hence the final machine will behave slightly differently than the simulations. In case of cyclotrons such discrepancies affect the overall turn pattern or may even alter the number of turns in the machine. Inaccuracies at the PSI Ring cyclotron facility that may harm the isochronism are compensated by additional magnetic fields provided by 18 trim coils. These are often absent from simulations or their implementation is very simplistic. In this paper a newly developed realistic trim coil model within the particle accelerator framework OPAL is presented that was used to match the turn pattern of the PSI Ring cyclotron. Due to the high-dimensional search space consisting of 48 design variables (simulation input parameters) and 182 objectives (i.e. turns) simulation and measurement cannot be matched in a straightforward manner. Instead, an evolutionary multi-objective optimization with a population size of more than 8000 individuals per generation together with a local search approach were applied that reduced the maximum absolute error to 4.5 mm over all 182 turns.

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Multiobjective optimization of dynamic aperture

Cited by 53 publications

References 13 publications

Machine based optimization using genetic algorithms in a storage ring

Machine based optimization using genetic algorithms in a storage ring

Online optimization of storage ring nonlinear beam dynamics

Matching of turn pattern measurements for cyclotrons using multiobjective optimization

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