The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

中文翻译：

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使用Jaya算法（MO-Jaya）的开关磁阻电机设计的多目标优化

开关磁阻电机（SRM）的设计与大多数其他电机的设计不同。SRM具有许多设计参数，这些参数与性能指标（即平均扭矩，效率等）具有非线性关系。因此，很难使用具有迭代方法的直接方程式设计SRM，这在其他机器上很常见。通过在搜索区域内搜索最佳变量值，使用了优化技术来克服这一挑战。本文采用多目标Jaya算法（MO-Jaya）实现了SRM设计的优化。在Jaya算法中，解决方案靠近最佳解决方案，而远离最差解决方案。因此，可以很好地增强搜索过程。而且，随机改变的参数实现了良好的搜索多样性。在本文中，建议还随机更改最佳和最差的解决方案。因此，如结果所示，实现了更好的多样性。使用MO-Jaya算法针对8/6和6/4 SRM进行了优化。使用的目标是平均扭矩，效率和铁重量。在相同条件和约束条件下，将MO-Jaya的结果与非支配排序遗传算法（NSGA-II）的结果进行比较。优化程序是用Lua编程语言编写的，并由FEMM4.2软件执行。结果表明，该方法成功实现了更好的目标值，进行了广泛的搜索并引入了各种最佳解决方案。使用MO-Jaya算法针对8/6和6/4 SRM进行了优化。使用的目标是平均扭矩，效率和铁重量。在相同条件和约束条件下，将MO-Jaya的结果与非支配排序遗传算法（NSGA-II）的结果进行比较。优化程序是用Lua编程语言编写的，并由FEMM4.2软件执行。结果表明，该方法成功实现了更好的目标值，进行了广泛的搜索并引入了各种最佳解决方案。使用MO-Jaya算法针对8/6和6/4 SRM进行了优化。使用的目标是平均扭矩，效率和铁重量。在相同条件和约束条件下，将MO-Jaya的结果与非支配排序遗传算法（NSGA-II）的结果进行比较。优化程序是用Lua编程语言编写的，并由FEMM4.2软件执行。结果表明，该方法成功实现了更好的目标值，进行了广泛的搜索并引入了各种最佳解决方案。优化程序是用Lua编程语言编写的，并由FEMM4.2软件执行。结果表明，该方法成功实现了更好的目标值，进行了广泛的搜索并引入了各种最佳解决方案。优化程序是用Lua编程语言编写的，并由FEMM4.2软件执行。结果表明，该方法成功实现了更好的目标值，进行了广泛的搜索并引入了各种最佳解决方案。