2134/25039
Zhao-Hua Liu
Zhao-Hua
Liu
Jing Zhang
Jing
Zhang
Shao-Wu Zhou
Shao-Wu
Zhou
Xiao-Hua Li
Xiao-Hua
Li
Kan Liu
Kan
Liu
Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM
Loughborough University
2017
Artificial immune system (AIS)
Coevolution
Elite population
Global search
Migration scheme
Parameter estimation
Particle swarm optimization (PSO)
Permanent-magnet syn- chronous machines (PMSMs)
Mechanical Engineering not elsewhere classified
Artificial Intelligence and Image Processing
2017-05-18 13:29:44
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Coevolutionary_particle_swarm_optimization_using_AIS_and_its_application_in_multiparameter_estimation_of_PMSM/9573011
In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE.