Optimization and experimental test of a miniature permanent magnet structure for a microfluidic magnetic resonance chip

Abstract

We propose a general global optimal algorithm to optimize the miniature permanent magnet structure of a micro magnetic resonance chip (μNMR-chip). For this purpose, we analyze the sensitivity of the permanent magnet structure to the design variables and determine the optimization variables. After this, radial basis function neural networks (RBFNNs) are constructed to model the objective functions, and the nondominated sorting genetic algorithm II (NSGA II) is improved by in- troducing a different weighting factor for each objective function in calculating the crowding distance. Combining the RBFNN with the improved NSGA II optimizes the miniature permanent magnet structure. Through comparison, the optimization solu- tions are proven effective. Finally, the optimized permanent magnet structure is manufactured and tested experimentally. After optimization, the volume of the permanent magnet block is reduced by 39%, and the permanent magnet becomes easier to manufacture.