  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.
