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

ZHANG Sen; ZHOU Yongquan

doi:10.1049/cje.2018.06.008

Volume 27 Issue 5

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ZHANG Sen and ZHOU Yongquan, “Grey Wolf Optimizer with Ranking-Based Mutation Operator for IIR Model Identification,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 1071-1079, 2018, doi: 10.1049/cje.2018.06.008

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ZHANG Sen and ZHOU Yongquan, “Grey Wolf Optimizer with Ranking-Based Mutation Operator for IIR Model Identification,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 1071-1079, 2018, doi: 10.1049/cje.2018.06.008

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

doi: 10.1049/cje.2018.06.008

ZHANG Sen¹,
ZHOU Yongquan^{1,2
,}

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

Funds: This work is supported by the National Natural Science Foundation of China (No.61463007, No.61563008).

More Information

Corresponding author: ZHOU Yongquan (corresponding author) received the M.S. degree in computer science from Lanzhou University, Lanzhou, China, in 1993 and the Ph.D. degree in computation intelligence from the Xidian University, Xi'an, China, in 2006. He is currently a professor at Guangxi University for Nationalities. His research interests include computation intelligence, neural networks, and intelligence information processing. (Email:yongquanzhou@126.com)
Received Date: 2016-01-22
Rev Recd Date: 2016-05-23
Publish Date: 2018-09-10

Abstract

Abstract

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.
- System identification,
- IIR system,
- Grey wolf optimizer,
- Ranking-based mutation operator

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References(36)

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