Layout optimization of corrector magnets for a proton therapy beamline via genetic algorithm

Liu, Xu; Chen, Qushan; Liao, Yicheng; Wang, Wei; Chen, Aote; Qin, Bin

doi:10.1016/j.nima.2020.164560

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Saturation power, the Pierce parameter and saturation length parameters were optimized. The NSGA-II algorithm was used on radiotherapy applications using collimating magnets on a proton accelerator, and the Pareto front was found for clinical requirements (Liu et al, 2020). Aydin et al (2020) made use of multiobjective optimization and evolutionary algorithms at the TARLA facility in Turkey.…”

Section: Introductionmentioning

confidence: 99%

Optimization of synchrotron radiation parameters using swarm intelligence and evolutionary algorithms

Karaca,

Bostanci,

Ketenoglu

et al. 2024

J Synchrotron Rad

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Alignment of each optical element at a synchrotron beamline takes days, even weeks, for each experiment costing valuable beam time. Evolutionary algorithms (EAs), efficient heuristic search methods based on Darwinian evolution, can be utilized for multi-objective optimization problems in different application areas. In this study, the flux and spot size of a synchrotron beam are optimized for two different experimental setups including optical elements such as lenses and mirrors. Calculations were carried out with the X-ray Tracer beamline simulator using swarm intelligence (SI) algorithms and for comparison the same setups were optimized with EAs. The EAs and SI algorithms used in this study for two different experimental setups are the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). While one of the algorithms optimizes the lens position, the other focuses on optimizing the focal distances of Kirkpatrick–Baez mirrors. First, mono-objective evolutionary algorithms were used and the spot size or flux values checked separately. After comparison of mono-objective algorithms, the multi-objective evolutionary algorithm NSGA-II was run for both objectives – minimum spot size and maximum flux. Every algorithm configuration was run several times for Monte Carlo simulations since these processes generate random solutions and the simulator also produces solutions that are stochastic. The results show that the PSO algorithm gives the best values over all setups.

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Section: Introductionmentioning

confidence: 99%