Grey Wolf Optimizer with Ranking-Based Mutation Operator for IIR Model Identification  

作　　者：ZHANG Sen ZHOU Yongquan 

机构地区：[1]College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China [2]School Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China

出　　处：《Chinese Journal of Electronics》2018年第5期1071-1079,共9页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61463007,No.61563008)

摘　　要：A variant of Grey wolf optimizer(GWO),called grey wolf optimizer with Ranking-based mutation operator(RGWO) is applied to the Infinite impulse response(IIR) system identification problem. RGWO makes GWO faster and more robust. In RGWO, the rankingbased mutation operator is integrated into the GWO to accelerate the convergence speed, and thus enhance the performance. The simulation results over several models are presented and statistically validated. Compared to other robust evolutionary algorithms, RGWO performs significantly better in terms of the quality, speed, and the stability of the final solutions.A variant of Grey wolf optimizer（GWO）,called grey wolf optimizer with Ranking-based mutation operator（RGWO） is applied to the Infinite impulse response（IIR） system identification problem. RGWO makes GWO faster and more robust. In RGWO, the rankingbased mutation operator is integrated into the GWO to accelerate the convergence speed, and thus enhance the performance. The simulation results over several models are presented and statistically validated. Compared to other robust evolutionary algorithms, RGWO performs significantly better in terms of the quality, speed, and the stability of the final solutions.

关 键 词：System identification IIR system Grey wolf optimizer Ranking-based mutation operator 

分 类 号：TN713[电子电信—电路与系统]