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A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm

by 1,2, 1, 1 and 1,2,*
1
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
2
Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2016, 9(1), 4; https://doi.org/10.3390/a9010004
Received: 6 November 2015 / Revised: 8 December 2015 / Accepted: 10 December 2015 / Published: 30 December 2015
Grey wolf optimization (GWO) is one of the recently proposed heuristic algorithms imitating the leadership hierarchy and hunting mechanism of grey wolves in nature. The aim of these algorithms is to perform global optimization. This paper presents a modified GWO algorithm based on complex-valued encoding; namely the complex-valued encoding grey wolf optimization (CGWO). We use CGWO to test 16 unconstrained benchmark functions with seven different scales and infinite impulse response (IIR) model identification. Compared to the real-valued GWO algorithm and other optimization algorithms; the CGWO performs significantly better in terms of accuracy; robustness; and convergence speed. View Full-Text
Keywords: complex-valued encoding; grey wolf optimization; diploid; test functions; IIR model identification complex-valued encoding; grey wolf optimization; diploid; test functions; IIR model identification
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Luo, Q.; Zhang, S.; Li, Z.; Zhou, Y. A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm. Algorithms 2016, 9, 4.

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