A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm

Qifang Luo^1,2, Sen Zhang¹, Zhiming Li¹ and Yongquan Zhou^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.

Received: 6 November 2015 / Revised: 8 December 2015 / Accepted: 10 December 2015 / Published: 30 December 2015

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Abstract

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.

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