Comparison of Multiobjective Optimization Methods for the LCLS-II Photoinjector

TL;DR

This paper compares three optimization algorithms for the LCLS-II photoinjector, demonstrating that model-based methods efficiently approximate the Pareto front, especially when starting from Latin hypercube samples, and provides recommendations for optimization strategies.

Contribution

It evaluates the performance of heuristic and model-based optimization algorithms for a complex particle accelerator component, highlighting the impact of initial sampling and objective penalties.

Findings

Latin hypercube sampling improves optimization performance

Model-based methods approximate the Pareto front with fewer evaluations

Heuristic methods are recommended for initial optimization stages

Abstract

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.