Magnetic force prediction of hybrid magnet with Halbach array using generalized regression neural network optimized by a modified aquila optimizer

Abstract

This paper addresses the balancing of global and local searches in the recently proposed AO (Aquila Optimizer) algorithm. The original random algorithm is modified using normal-distribution parameters, and an adaptive function represented by a Weibull function is added to the motion law of the predator. Sixteen benchmark functions are used to test the improved algorithm against several recently developed algorithms. The results show that the accuracy and convergence speed of the modified algorithm are improved while the advantages of the original algorithm are retained. In solving the problems of a complex calculation and limited solution in the design of a hybrid electromagnetic structure based on a Halbach array, a prediction model based on the improved algorithm and generalized regression neural network (GRNN) is designed for improved prediction accuracy of the GRNN. Thirty groups of data are obtained using Ansoft, and the prediction accuracy of the improved GRNN is verified using the data. The mean squared error (MSE) of normalized prediction results reaches 0.1404. The improved prediction model has the prediction error less than 10% and its performance is better than the RBF and the KCV-GRNN.