Comparison of multiobjective optimization methods for the LCLS-II photoinjector

Particle accelerators are among some of the largest science experiments in the world and can consist of thousands of components with a wide variety of input ranges. These systems can easily become unwieldy optimization problems during design and operations studies. Starting in the early 2000s, searching for better beam dynamics configurations became synonymous with heuristic optimization methods in the accelerator physics community. Genetic algorithms and particle swarm optimization are currently the most widely used. These algorithms can take thousands of simulation evaluations to find optimal solutions for one machine prototype. For large facilities such as the Linac Coherent Light Source (LCLS) and others, this equates to a limited exploration of many possible design configurations. In this paper, the LCLS-II photoinjector is optimized with three optimization algorithms. All optimizations were started from both a uniform random and Latin hypercube sample. In all cases, the optimizations started from Latin hypercube samples outperformed optimizations started from uniform samples. All three algorithms were able to optimize the photoinjector, with the model-based methods approximating the Pareto front in fewer simulation evaluations. This work, in combination with previous optimization observations, indicates objective penalties have a strong impact on the efficiency of such methods. In general, we recommend heuristic methods for initial optimizations and model-based methods when information about the objective space is available.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Particle accelerators are complex systems with numerous components and wide input ranges, making optimization a challenging task. The use of heuristic optimization methods, such as genetic algorithms and particle swarm optimization, has become prevalent in the field of accelerator physics. However, their efficiency is limited by the large number of simulation evaluations required. In this study, the LCLS-II photoinjector was optimized using three different algorithms, with the Latin hypercube samples outperforming the uniform samples. Model-based methods approximated the Pareto front with fewer simulation evaluations. This work highlights the importance of objective penalties and recommends heuristic methods for initial optimizations and model-based methods when information about the objective space is available.