Online storage ring optimization using dimension-reduction and genetic algorithms

TL;DR

This paper presents an approach for online optimization of particle storage rings by reducing the parameter space dimension and applying genetic algorithms, achieving efficient tuning and improved emittance control at CESR.

Contribution

It introduces a dimension-reduction technique combined with genetic algorithms for high-dimensional storage ring optimization, demonstrating practical effectiveness at CESR.

Findings

Dimension reduction from 81 to 8 parameters enables efficient optimization.

Genetic algorithms achieve emittance improvements comparable to state-of-the-art methods.

Enhanced control over orbit errors during optimization.

Abstract

Particle storage rings are a rich application domain for online optimization algorithms. The Cornell Electron Storage Ring (CESR) has hundreds of independently powered magnets, making it a high-dimensional test-problem for algorithmic tuning. We investigate algorithms that restrict the search space to a small number of linear combinations of parameters ("knobs") which contain most of the effect on our chosen objective (the vertical emittance), thus enabling efficient tuning. We report experimental tests at CESR that use dimension-reduction techniques to transform an 81-dimensional space to an 8-dimensional one which may be efficiently minimized using one-dimensional parameter scans. We also report an experimental test of a multi-objective genetic algorithm using these knobs that results in emittance improvements comparable to state-of-the-art algorithms, but with increased control over orbit errors.