ANL, Lemont, Illinois, USA
SLAC, Menlo Park, California, USA
While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front end beam dynamics has been difficult to model. One of the issues is that beam properties from the thermionic gun can vary from time to time. As a result, linac front end beam tuning is required to establish good matching and maximize the charge transported through the linac. We have been using a traditional simplex optimizer to find the best settings for the gun front end magnets and steering magnets. However, it takes a long time and requires some fair initial conditions. Therefore, we imported other on-line optimizers, such as robust conjugate direction search (RCDS) which is a classic optimizer as simplex, multi-objective particle swarm (MOPSO), and multi-generation gaussian process optimizer (MG-GPO) which is based on machine learning technique. In this paper we report our experience with these on-line optimizers for maximum bunch charge transportation efficiency through the linac.
SLAC, Menlo Park, California, USA
We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other recent preselection-assisted algorithms.
SLAC, Menlo Park, California, USA
Optimization algorithms/techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and Gaussian process (GP) have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, sometimes even unrealistic, since the algorithms and problems could be implemented in different languages, might require specific resources, or have physical constraints. We introduce an optimization platform named Teeport that is developed to address the above issue. This real-time communication (RTC) based platform is particularly designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.