Machine based optimization using genetic algorithms in a storage ring
Kai Tian,James Safranek,Y.T.Yan +2 more
TL;DR: In this article, the beam loss rate is chosen as the sole objective function, which is inversely proportional to the vertical beam size and can be measured instantaneously in SPEAR3.
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Abstract: 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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Citations
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.
Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson–Segalman fluid on vertical cylinder for drainage problems
TL;DR: Results of statistical analysis in terms of performance measures based on mean, standard deviation, mean absolute deviation, root mean square error and Nash–Sutcliffe efficiency as well as their global variations further established the worth of the given scheme for each variant of drainage problem.
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Evaluation of Machine Learning Methods for LHC Optics Measurements and Corrections Software
Elena Fol
- 19 Mar 2018
TL;DR: To identify the category of tasks which can be performed by machines in the domain of optics measurements and correction on the Large Hadron Collider is one of the central research subjects of this thesis.
Online storage ring optimization using dimension-reduction and genetic algorithms
TL;DR: In this paper, the authors investigate algorithms that restrict the search space to a small number of linear combinations of parameters ("knobs") which contain most of the effect on the chosen objective (the vertical emittance), thus enabling efficient tuning.
14
Review of Linear Optics Measurements and Corrections in Accelerators
Rogelio Tomás,M. Aiba,Andrea Franchi,Ubaldo Iriso +3 more
- 01 Jan 2016
TL;DR: A review of the existing techniques is presented in this article, highlighting comparisons, merits and limitations of the proposed techniques, as well as the advantages of using the beam as a diagnostic tool for beam-based optimization of performance related observables.
10
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