Parameter identification of Jiles–Atherton model for magnetostrictive actuator using hybrid niching coral reefs optimization algorithm

Abstract

The Jiles–Atherton model is widely applied in the description of hysteresis in ferromagnetic, ferroelectric, magnetostrictive and piezoelectric materials, however the parameter identification of it constitutes a challenging problem. In this paper, a hybrid niching coral reefs optimization algorithm (HNCRO) is proposed and implemented for parameter identification of the Jiles–Atherton model for magnetostrictive actuator. To prevent the algorithm from converging to the local optimum, the niche technology based on fitness sharing is introduced into original coral reefs optimization algorithm (OCRO). In order to enhance the local search capability of OCRO, Rosenbrock's rotational direction method is applied to refine the best solution. Compared with OCRO, genetic algorithm, particle swarm optimization, hybrid particle swarm optimization and gravitational search algorithm and differential evolution algorithm with a hybrid mutation operator, the proposed algorithm has better performance in terms of convergence speed, success rate, and accuracy in benchmark functions test. At last, experiments are carried out to verify the effectiveness of the proposed approach on a magnetostrictive actuator. The results demonstrate that the HNCRO is a promising method for parameter identification of the Jiles–Atherton model.