基于多种群遗传算法的永磁涡流驱动器的多目标优化设计  

Multi-Objective Optimization Design of PMECD by Multiple Population Genetic Algorithm

作　　者：时统宇[1] 王大志[1] 李召[1] 

机构地区：[1]东北大学信息科学与工程学院,沈阳110819

出　　处：《电工技术学报》2016年第A02期262-268,共7页Transactions of China Electrotechnical Society

摘　　要：为了优化永磁涡流驱动器的几个关键结构参数,研究了基于多种群遗传算法的多目标优化算法。首先,在磁场分析模型的基础上,推导出关键结构参数的解析表达式。以永磁体厚度、极弧系数和铜盘厚度为变量,以输出转矩、转动惯量和驱动器体积为优化目标,提出了基于熵值权重的永磁驱动器多目标优化函数,然后应用多种群遗传算法对永磁涡流驱动器进行优化。通过三维有限元仿真和实验验证了优化结果的准确性和可行性。最后,将计算结果与其他两种优化方法得到的结果进行了对比。结果表明,相比其他优化算法,该基于解析模型的多种群遗传算法在结构参数优化设计中有更好的计算效果。The aim of this paper was to explore the use of the multiple population genetic algorithm （MPGA） to optimize several parameters of permanent magnet eddy current drivers . At first, on the basis of the magnetic field analysis model, the analytical formulas of key parameters were deduced. By using permanent magnet thickness, pole-arc coefficient and copper plate thickness as variables and taking output torque, rotational inertia and the volume of the driver as optimization goals, this paper proposed a multi-objective optimization function with entropy coefficients and used the multiple population genetic algorithm to optimize parameters structure of the driver. Then, 3D finite element analysis （3D-FEA） and experimental results proved the validity and feasibility of the proposed method. The results confirm that compared with other two optimization algorithms, optimization design result by the multiple population genetic algorithm based on the analytical model has better effect on optimization of structural parameters.

关 键 词：永磁涡流驱动器 解析法 熵值权重 多目标优化 多种群遗传算法 

分 类 号：TM144[电气工程—电工理论与新技术]