中文摘要：

特殊的结构和控制方法使得开关磁阻电机（SRM）具有多变量、强耦合、高非线性等特点，如何快速而准确的得到最优设计方案一直是研究的热点与难点。首先将粒子群算法（PSO）与文化算法（CA）相结合，构建了文化粒子群优化算法（CPSOA），通过将PSO嵌入到CA架构，实现了不同空间群体的并行进化，提高了PSO的优化精度与效率。然后，采用传统设计方法得到了SRM的初始设计方案，并进行了初步校核。最后，针对一定的目标和约束，以参数对性能的影响模式为基础，利用CPSOA对初始方案进行了优化，得到了关键几何尺寸和控制参数的全局最优解。

英文摘要：

A switched reluctance machine （SRM） has multiple variables, strong coupling and highly nonlinear characteristics due to its special structure and control method. This paper constructs the cultural particle swarm opti- mization algorithm （CPSOA） by combining particle swarm optimization （PSO） and cultural algorithm （CA）. By embedding the PSO into the CA framework, it realized the parallel evolution of population in different spaces and thus enhances the accuracy and efficiency of the PSO. Then it obtains the initial design scheme of the SRM with the traditional design method and carries out its preliminary tests. Finally, with certain objectives and constraints and on the basis of the parameters＇ influence mode on performances, it uses CPSOA to optimize the initial design scheme, thus obtaining the globally optimal solutions of key geometric dimensions and control parameters.