Genetic algorithm diagnosis of individual cell frequencies in a coupled cavity chain

Ni, Yi; Tong, Dechun; Lin, Y.

doi:10.1016/s0168-9002(00)01327-9

Cited by 4 publications

(5 citation statements)

References 3 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%

“…knobs, to be adjusted for optimum search. In spite of various approaches used in previous efforts, all of them evaluate the objective functions based on numerical simulations using particle tracking codes such as ELEGANT [17], which has been used for optimizing the lattice of the SPEAR3 storage ring [18], or analytical models, such as an equivalent circuit of accelerating cavities [12].…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…A superconducting magnet design tool developed at CERN produced a collection of magnet designs without meshing the superconducting coils and allowed the magnet designers to explore several possible designs subject to various engineering constraints [12,13]. GA-based tools have been used to design accelerator cavities meeting resonance and higher-order mode frequency requirements [14,15] and surface field constraints [16,17] and to diagnose a detuned SRF cavity installed in a multicavity cryounit [18].…”

Section: Introductionmentioning

confidence: 99%

Innovative applications of genetic algorithms to problems in accelerator physics

Hofler

Terzić

Krämer

et al. 2013

Phys. Rev. ST Accel. Beams

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The genetic algorithm (GA) is a powerful technique that implements the principles nature uses in biological evolution to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing Continuous Electron Beam Accelerator Facility nuclear physics machine, the proposed Medium-energy Electron-Ion Collider at Jefferson Lab, and a radio frequency gun-based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, include a newly devised enhancement which leads to improved convergence to the optimum, and make recommendations for future GA developments and accelerator applications.

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“…However, this also has the difficult requirement to set the value of a factor to control the mutational amplitude, as discussed in the Section IV. Yi et al [16] proposed a mutation mechanism that applies a local search algorithm based on gradients of fitness. Since these modifications also have the drawback of local optima, it is necessary to tune the factor to control the GA.…”

Section: Adaptive Domain Methods (Adm)mentioning

confidence: 99%

“…The gene of the offspring is computed according to (16) where is a uniform random number , and is the ratio of the mutational amplitude to the parameter domain, which is constant in the system as . In this paper, we denote this mutation as "fluctuation."…”

Section: ) Crossovermentioning

confidence: 99%

Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms

Tomioka

Nisiyama

Enoto

2007

IEEE Trans. Evol. Computat.

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Abstract-In conventional least square (LS) regressions for nonlinear problems, it is not easy to obtain analytical derivatives with respect to target parameters that comprise a set of normal equations. Even if the derivatives can be obtained analytically or numerically, one must take care to choose the correct initial values for the iterative procedure of solving an equation, because some undesired, locally optimized solutions may also satisfy the normal equation.In the application of genetic algorithms (GAs) for nonlinear LS, it is not necessary to use normal equations, and a GA is also capable of avoiding localized optima. However, convergence of population and reliability of solutions depends on the initial domain of parameters, similarly to the choice of initial values in the abovementioned method using the normal equation. To overcome this disadvantage of applying GAs for nonlinear LS, we propose to use an adaptive domain method (ADM) in which the parameter domain can change dynamically by using several real-coded GAs with short lifetimes.Through an example problem, we demonstrate improvements in terms of both the convergence and the reliability by ADM. A further merit in the proposed method is that it does not require any specialized knowledge about GAs or their tuning. Therefore, the nonlinear LS by ADM with GAs are accessible to general scientists for various applications in many fields.Index Terms-Adaptive domain, convergence, real-coded genetic algorithm (GA), multimodal problem, nonlinear least square (LS) regression, reliability.

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Genetic algorithm diagnosis of individual cell frequencies in a coupled cavity chain

Cited by 4 publications

References 3 publications

Machine based optimization using genetic algorithms in a storage ring

Machine based optimization using genetic algorithms in a storage ring

Innovative applications of genetic algorithms to problems in accelerator physics

Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms

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