Quadrupole Magnet Design based on Genetic Multi-Objective Optimization

TL;DR

This paper presents a method for optimizing quadrupole magnet geometry using a multi-objective genetic algorithm, balancing magnetic field quality and cost efficiency through finite element analysis.

Contribution

It introduces the application of NSGA-III to quadrupole magnet design, integrating finite element modeling for multi-objective optimization.

Findings

Optimized magnet designs with improved field quality.

Trade-offs between cost and magnetic performance identified.

Pareto front analysis reveals optimal design solutions.

Abstract

This work suggests to optimize the geometry of a quadrupole magnet by means of a genetic algorithm adapted to solve multi-objective optimization problems. To that end, a non-domination sorting genetic algorithm known as NSGA-III is used. The optimization objectives are chosen such that a high magnetic field quality in the aperture of the magnet is guaranteed, while simultaneously the magnet design remains cost-efficient. The field quality is computed using a magnetostatic finite element model of the quadrupole, the results of which are post-processed and integrated into the optimization algorithm. An extensive analysis of the optimization results is performed, including Pareto front movements and identification of best designs.