Improvement of machine learning enhanced genetic algorithm for nonlinear beam dynamics optimization

Wan, Jinyu; Chu, P. C.; Jiao, Yi; Li, Yongjun

doi:10.1016/j.nima.2019.162683

Cited by 31 publications

(15 citation statements)

References 13 publications

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“…For the CEMOGA, the resulting diversity is similar to, and sometimes lower than, that of the standard MOGA. This is probably because the replacement of original data with the individuals repopulated in a reduced variable range may decrease the diversity [15]. For the combination of the MOPSO and MOGA, the obtained diversity is slightly higher than that with MOPSO in most cases of optimizing ZDT2 but becomes lower than that of MOPSO when optimizing ZDT1 and ZDT6.…”

Section: Optimizations Of Three Test Problemsmentioning

confidence: 96%

“…For the CEMOGA, the ratio of the individual replacement is set to be the same as in Ref. [15], 20%. For the MOPSO, the same parameter settings as in Ref.…”

Section: Optimizations Of Three Test Problemsmentioning

confidence: 99%

“…And, a combination of MOGA and MOPSO has more potential of avoiding local optima than using the MOGA or MOPSO alone [12]. Moreover, accelerator scientists are now exploring different machine learning enhanced MOGAs [14][15][16][17][18]. The basic consideration is that the dataset fX n ; Y n g continuously produced and accumulated by the MOGA can be used as the training data of machine learning, so as to reveal some hidden properties of the data which, in turn, helps to speed up the convergence and/or increase the diversity among solutions.…”

Section: Introductionmentioning

confidence: 99%

“…One approach is to combine MOGA with data clustering (e.g., [14,15]), namely, using K-means clustering for each generation to find the "elite" variable range covered by the individuals that have high objective performance. The new competitive offspring individuals are then generated within the elite variable range and used to replace the original data in the population, which can result in faster convergence.…”

Section: Introductionmentioning

confidence: 99%

“…To demonstrate the effectiveness of NBMOGA, taking three classic optimization problems as test problems, the convergence rate and diversity available with the NBMOGA, the standard MOGA, and three other multiobjective optimization methods that have been proposed and used in the accelerator field, i.e., the MOPSO, the combination of MOPSO and MOGA, and the clustering enhanced MOGA (CEMOGA) [15], are quantitatively compared in Sec. II.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Neural network-based multiobjective optimization algorithm for nonlinear beam dynamics

Wan

Chu

Jiao

2020

Phys. Rev. Accel. Beams

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Multiobjective genetic algorithms (MOGAs) have proven to be powerful in solving multiobjective problems in the accelerator field. Nevertheless, for explorative problems that have many variables and local optima, the performance of MOGAs is not always satisfactory, especially when a small population size is used due to practical limitations, e.g., limited computing resources. To deal with this challenge, in this paper an enhanced MOGA, neural network-based MOGA (NBMOGA), is proposed. In this method, the data produced with the standard MOGA are used to train a neural network. The neural network is fast to produce a large pool of objective function estimates, with sufficiently high accuracy. A subset of the most competitive estimates is selected to form a population (matching MOGA population size), which is then evaluated with the MOGA evaluator. By taking three classic multiobjective problems as examples, we demonstrate that the proposed method promises a faster convergence and a higher degree of diversity than that available with the standard MOGA and other three optimization methods that have been applied in the accelerator field, i.e., the multiobjective particle swarm optimization (MOPSO), the combination of MOPSO and MOGA, and the clustering enhanced MOGA. And then this method is applied to a timeconsuming optimization problem, the dynamic aperture and Touschek lifetime optimization of the high energy photon source. It turns out that, within the same optimization time, a better set of solutions in the objective space can be obtained with the NBMOGA than using other methods. The Touschek lifetime can be improved by about 10% compared with using the standard MOGA, with approximately the same dynamic aperture area. Besides, a higher degree of diversity among solutions is observed with the NBMOGA than using other tested methods.

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Section: Optimizations Of Three Test Problemsmentioning

confidence: 96%

“…For the CEMOGA, the ratio of the individual replacement is set to be the same as in Ref. [15], 20%. For the MOPSO, the same parameter settings as in Ref.…”

Section: Optimizations Of Three Test Problemsmentioning

confidence: 99%