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.