Design Optimization of Multipole Galatea Trap Coils by Multiple Population Genetic Algorithm

In order to improve the performance of multipole Galatea traps, this paper proposes an optimized method for the design of coil parameters. Based on an accurate magnetic field model, key parameters describing the multipole Galatea magnetic trap configuration were analyzed to establish an optimization model using the axial electromagnetic force, weak magnetic field area, and average magnetic mirror ratio as the optimization goals with the coil current as the design variable. Applying the improved multiple population genetic algorithm (MPGA), which has a strong searching ability and a fast convergence speed, enables production of optimization results following selection of the appropriate weight coefficients. Results confirm that optimization design results from MPGA are consistent with the design goals for different weight coefficients. In addition, the performance of multipole Galatea magnetic traps with optimization coil parameters was improved. Finally, the results from finite-element simulation software proved the validity and feasibility of the proposed method.