Simulation Study Of An Analytic Preisach Hysteresis Model Based On An Improved Chimpanzee Optimization Algorithm

The analytical Preisach hysteresis model solves the problems of large measurement error and numerical instability caused by discrete Everett function in the Preisach hysteresis model,but the analytical Preisach hysteresis model also has many parameters and complex identification problems.To solve these problems,a multi-strategy improved Chimp optimization algorithm combining differential variation and random variation is proposed in this paper to achieve fast and accurate parameter identification of analytic Preisach hysteresis model.Based on the identification results,the magnetic characteristics of electrical magnetic materials were simulated.The main work was completed as follows:(1)Literature research was conducted on existing Preisach hysteresis models and their parameter identification methods,and an analytical Preisach hysteresis model with magnetic field intensity H as the input variable was established,which provided a model basis for later model parameter identification and magnetic property simulation research of magnetic materials.(2)A multi-strategy improved Chimpanzee optimization algorithm(IM-COA algorithm)integrating differential variation and random variation was proposed.The algorithm adopted the improved logistic mapping mixed dynamic reverse learning strategy to initialize the population,which enriched the population diversity.The global exploration and local development ability of dynamic nonlinear decline factor and adaptive weight factor balance algorithm are used,and then differential variation strategy is used to enhance the information exchange among individuals and expand the search scope.Finally,random variation perturbation strategy is used to further enhance the ability of the algorithm to jump out of the local optimal.(3)The proposed improved chimpanzee optimisation algorithm is simulated and validated for practical applications.Firstly,various algorithm simulation experiments and Wilcoxon rank sum tests are carried out based on 12 standard test functions and some CEC2014 test functions to verify the effectiveness of the algorithm from multiple perspectives,and finally the algorithm is applied to practical engineering case studies to verify the practical application value of the algorithm.(4)Research on parameter identification method of analytical Preisach hysteresis model based on improved optimization algorithm.In this paper,the improved algorithm is applied to the parameter identification of the analytical Preisach model,and the simulation results are verified,and the practicability of the algorithm is verified,which lays a foundation for the experimental verification of the improved algorithm.(5)Simulation of analytical Preisach hysteresis model based on improved Chimpanzee algorithm.Firstly,the parameters of the analytical Preisach hysteresis model are identified by using the improved chimp optimization algorithm proposed in this paper.Secondly,based on the identification results,the analytical Preisach hysteresis model is used to simulate the magnetic characteristics of oriented silicon steel B50A800.The comparison with the experimental results shows that the simulated hysteresis loop is in good agreement with the measured hysteresis loop.The practicability of this algorithm in the simulation of magnetic properties of magnetic materials is verified.Finally,the main work of this paper is summarized and summarized,and according to the achievements and deficiencies of this paper,the follow-up work to be carried out.