Comparison of Multiobjective Optimization Methods for the LCLS-II Photoinjector

• Published in: arXiv.org
• Main Authors: Neveu, Nicole, Chang, Tyler H, Paris, Franz, Hudson, Stephen, Larson, Jeffrey
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 31.10.2022
• Subjects: Algorithms; Coherent light; Configuration management; Design optimization; Genetic algorithms; Heuristic methods; Hypercubes; Light sources; Multiple objective analysis; Optimization; Particle accelerators; Particle swarm optimization; Physics - Accelerator Physics
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2206.03882

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.