Performance of automated synchrotron lattice optimisation using genetic algorithm

Abstract

Rapid advances in superconducting magnets and related accelerator technology opens many unexplored possibilities for future synchrotron designs. We present an efficient method to probe the feasible parameter space of synchrotron lattice configurations. Using this method, we can converge on a suite of optimal solutions with multiple optimisation objectives. It is a general method that can be adapted to other lattice design problems with different constraints or optimisation objectives. In this method, we tackle the lattice design problem using a multi-objective genetic algorithm. The problem is encoded by representing the components of each lattice as columns of a matrix. This new method is an improvement over the neural network based approach in terms of computational resources. We evaluate the performance and limitations of this new method with benchmark results.