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《电子学报(英文)》 2018-05
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Grey Wolf Optimizer with Ranking-Based Mutation Operator for IIR Model Identification

ZHANG Sen;ZHOU Yongquan;College of Information Science and Engineering,Guangxi University for Nationalities;School Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence;  
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.
【Fund】: supported by the National Natural Science Foundation of China(No.61463007 No.61563008)
【CateGory Index】: TN713
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