Multiobjective Exponential Particle Swarm Optimization Approach Applied to Hysteresis Parameters Estimation

The term “swarm intelligence” is used to describe algorithms and distributed problem solvers inspired by the collective behavior of insect colonies and other animal societies. Particle swarm optimization (PSO) is a kind of swarm intelligence that is based on the social behavior metaphor. Furthermore, PSO is a stochastic search technique with reduced memory requirement, computationally effective and easier to implement compared to other optimization metaheuristics. Unlike the traditional optimization algorithms, PSO is a derivative-free algorithm and thus it is especially effective in dealing with complex and nonlinear problems in electromagnetic optimization applications. In this paper, a multiobjective PSO approach based on exponential distribution probability operator (MOPSO-E) is proposed and evaluated. Numerical comparisons with results using a multiobjective PSO with external archiving and the proposed MOPSO-E demonstrated that the performance of the MOPSO-E is promising in Jiles-Atherton vector hysteresis model parameter identification. The proposed MOPSO-E to find nondominated solutions that represent the good trade-offs among the objectives in the evaluated case study.