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Solution of Jiles-Atherton vector hysteresis parameters estimation by modified Differential Evolution approaches

机译：改进的差分进化方法求解Jiles-Atherton矢量滞后参数估计

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Differential Evolution (DE) is a simple and efficient stochastic global optimization algorithm of evolutionary computation field, which involves the evolution of a population of solutions using operators such as mutation, crossover, and selection. The basic idea of DE is to adapt the search during the evolutionary process. At the start of the evolution, the perturbations are large since parent populations are far away from each other. As the evolutionary process matures, the population converges to a small region and the perturbations adaptively become small. DE approaches have been successfully applied to solve a wide range of optimization problems. In this paper, the parameters set of the Jiles-Atherton vector hysteresis model is obtained with an approach based on modified Differential Evolution (MDE) approaches using generation-varying control parameters based on generation of random numbers with uniform distribution. Several evaluated MDE approaches perform better than the classical DE methods and a genetic algorithm approach in terms of the quality and stability of the final solutions in optimization of vector Jiles-Atherton vector hysteresis model from a workbench containing a rotational single sheet tester.
机译：差分进化（DE）是进化计算领域的一种简单有效的随机全局优化算法，它涉及使用算子（如变异，交叉和选择）对一组解决方案进行进化。 DE的基本思想是在进化过程中适应搜索。在进化的开始，由于父母群体彼此相距遥远，所以扰动很大。随着进化过程的成熟，种群会聚到一个很小的区域，并且摄动会自适应地变小。 DE方法已成功应用于解决各种优化问题。本文采用基于改进的差分进化（MDE）方法的方法获得了Jiles-Atherton矢量磁滞模型的参数集，该方法使用了基于均匀分布随机数的生成的可变生成控制参数。在优化包含旋转单张测试仪的工作台上的矢量Jiles-Atherton矢量滞后模型的最终解决方案的质量和稳定性方面，一些经过评估的MDE方法的性能优于经典DE方法和遗传算法。

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