基于多目标遗传算法的高频磁致伸缩换能器优化设计

磁致伸缩换能器在高频激励下存在铁心涡流损耗大、磁场分布不均匀、电磁转化效率低等问题，需要从换能器本体优化设计方面寻求解决。首先对换能器的线圈高度和磁轭回路结构进行仿真分析以初步确定磁路结构；然后基于非支配排序遗传算法对换能器提出了一个整体的多目标优化设计模型，该模型以增大磁致伸缩棒内磁场强度、提高棒内的磁场分布均匀度和减少换能器高频损耗为优化目标，引入规范化排序和熵权法对该优化方法得到的Pareto前沿解进行决策支持，筛选一组最优设计方案；最后对该最优解进行仿真分析，磁场分布和数值计算结果验证了该优化方法的有效性，根据优化结果制作了一台换能器样机，样机输出特性的测试结果表明了优化设计方法的可行性。

Optimization design of high frequency magnetostrictive transducer based on the multi-objective genetic algorithm

Magnetostrictive transducers have high core eddy current loss, uneven magnetic field distribution and low electromagnetic conversion efficiency under high frequency excitation. These issues need to be addressed by the optimal design of the transducer body. The coil height and yoke loop structure of the transducer are firstly simulated to initially determine the magnetic circuit structure. Then, an overall multi-objective optimization design model for the transducer is proposed, which is based on the non-dominated ranking genetic algorithm. The optimization objectives are to increase the magnetic field strength in the magnetostrictive bar, improve the uniformity of the magnetic field distribution in the bar, and reduce the high frequency loss of the transducer. The normalized ranking and entropy weighting methods are introduced for decision support of the Pareto front solutions obtained by this optimization method to screen a set of optimal design solutions. Finally, the optimal solution is simulated and analyzed. Results of magnetic field distribution and numerical calculation verify the effectiveness of the optimization method.