Title :
Multiobjective Exponential Particle Swarm Optimization Approach Applied to Hysteresis Parameters Estimation
Author :
Coelho, Leandro Dos S ; Guerra, Fábio A. ; Leite, Jean V.
Author_Institution :
PPGEPS, Pontifical Catholic Univ. of Parana (PUCPR), Curitiba, Brazil
Abstract :
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.
Keywords :
exponential distribution; hysteresis; optimisation; particle swarm optimisation; Jiles-Atherton vector hysteresis model; electromagnetic optimization applications; exponential distribution probability and operator; hysteresis parameters; insect colonies; multiobjective exponential particle swarm optimization approach; optimization approach; optimization metaheuristics; social behavior metaphor; stochastic search technique; swarm intelligence; traditional optimization algorithms; Magnetic hysteresis; Materials; Mathematical model; Optimization; Particle swarm optimization; Saturation magnetization; Vectors; Electromagnetics; evolutionary computation; optimization; swarm intelligence;
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2011.2172581