This paper proposes a novel hybrid optimization algorithm that combines linear programming (LP), genetic algorithm (GA), and nonlinear programming (NLP) to achieve the optimal design of highly homogeneous superconducting magnets for MRI systems. Initially, the predetermined rectangular region is divided into an array of superconductor coils. Then, linear programming is utilized to minimize the consumption of superconducting conductors as the objective function and to obtain the nonzero current regions by considering the field peak-to-peak uniformity in the diameter of the spherical volume (DSV) and the range of the 5 Gauss stray field as constraints. Subsequently, the genetic algorithm is employed to convert the nonzero current regions into coils with rectangular cross-sections. Finally, the NLP is applied to adjust the position of each coil to obtain the magnet criteria. An illustrative example is provided: an actively shielded MRI superconducting magnet with a center field strength of 1.5 T. The effectiveness of this optimization method is demonstrated through the design, electromagnetic analysis, and stress analysis conducted on this example.

中文翻译：

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基于新型混合优化方法的MRI超导磁体电磁设计

本文提出了一种新颖的混合优化算法，结合线性规划（LP）、遗传算法（GA）和非线性规划（NLP）来实现MRI系统的高度均匀超导磁体的优化设计。最初，预定矩形区域被划分为超导线圈阵列。然后，利用线性规划以最小化超导导体的消耗为目标函数，并通过考虑球体直径（DSV）的场峰峰均匀性和5的范围来获得非零电流区域。高斯杂散场作为约束。随后，采用遗传算法将非零电流区域转换为矩形横截面的线圈。最后，应用自然语言处理来调整每个线圈的位置以获得磁体标准。给出了一个说明性的例子：中心场强为1.5T的主动屏蔽MRI超导磁体。通过对该例子进行的设计、电磁分析和应力分析证明了这种优化方法的有效性。