GENETIC ALGORITHMS FOR THE OPTIMAL DESIGN OF SUPERCONDUCTING ACCELERATOR MAGNETS

The paper describes the use of genetic algorithms with the concept of niching for the optimal design of superconduct- ing magnets for the Large Hadron Collider, LHC at CERN. The method provides the designer with a number of local optima which can be further examined with respect to ob- jectives such as ease of coil winding, sensitivity to manu- facturing tolerances and local electromagnetic force distri- bution. A 6 block dipole coil was found to have advantages compared to the standard 5 block version which was previ- ously designed using deterministic optimization methods. Results were proven by a short model magnet recently built and tested at CERN.