Determination of a Hysteresis Model Parameters with the Use of Different Evolutionary Methods for an Innovative Hysteresis Model

Abstract

For precise modeling of electromagnetic devices, we have to model material hysteresis. A Genetic Algorithm, Differential Evolution with three different strategies, teaching–learning-based optimization and Artificial Bee Colony, were used for testing seven different modified mathematical expressions, and the best combination of mathematical expression and solving method was used for hysteresis modeling. The parameters of the hysteresis model were determined based on the measured major hysteresis loop and first-order reversal curves. The model offers a simple determination of the magnetization procedure in the areas between measured curves, with the only correction of two parameters based on only two known points in the magnetization process. It was tested on two very different magnetic materials, and results show good agreement between the measured and calculated curves. The calculated curves between the measured curves have correct shapes. The main difference between our model and other models is that, in our model, each measured curve, major and reversal, is described with different parameters. The magnetization process between measured curves is described according to the nearest measured curve, and this ensures the best fit for each measured curve. In other models, there is mainly only one curve, a major hysteresis or magnetization curve, used for the determination of the parameters, and all other curves are then dependent on this curve. Results confirm that the evolutionary optimization method offers a reliable procedure for precise determination of the parameters.

Application of the Differential Evolutionary Algorithm to the Estimation of Pipe Embedding Parameters

“…DE was chosen mainly because the algorithm does not have patent protection, which means it is possible to popularize the soil parameter estimation method. DE was put forward by Storn and Price [ 23 ] in 1995 and has gained wide applications [ 24 , 25 , 26 ].…”

Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

“…In the context of the subject of the present paper it is worth to notice that a somewhat similar identification problem in civil engineering (parameter estimation of internal thermal mass of building dynamic models for prediction of transient heating or cooling in a distributed network model of a building) has been tackled successfully in the paper by [26]. In work [27], the parameters of the hysteresis model for both soft and hard ferromagnets were estimated using the genetic algorithm method. The authors compared the performance of three meta-heuristic approaches (Genetic Algorithms and two more recent optimization techniques, namely Differential Evolution and Artificial Bee Colony).…”

Estimating the Strain-Rate-Dependent Parameters of the Johnson-Cook Material Model Using Optimisation Algorithms Combined with a Response Surface

“…The maximum number of iterations or generations was set to 10,000 in order to stop the algorithm. Each algorithm was then repeated 30 times, which was similar in the literature [39], where also comparisons between different algorithm's performances were carried out. Other settings for the applied algorithms were equal as in the previous studies [31] and are listed in Table 5.…”