Grey Wolf Optimization (GWO) Algorithm
This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-heuristic algorithms. First, a brief literature review is presented and then the natural process of the GWO algorithm is described. Also, the optimization process and
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Grey Wolf Optimization (GWO) Algorithm Hossein Rezaei, Omid Bozorg-Haddad and Xuefeng Chu
Abstract This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-heuristic algorithms. First, a brief literature review is presented and then the natural process of the GWO algorithm is described. Also, the optimization process and a pseudo code of the GWO algorithm are presented in this chapter.
9.1
Introduction
Grey wolf optimization (GWO) is one of the new meta-heuristic optimization algorithms, which was introduced by Mirjalili et al. (2014). Gholizadeh (2015) developed the GWO algorithm to solve an optimization problem of double-layer grids considering the nonlinear behavior. The results illustrated that GWO had a better performance than other algorithms in finding the optimal design of nonlinear double-layer grids. Mirjalili (2015) used the GWO algorithm to learn multi-layer perceptron (MLP) for the first time. In the study, the results of GWO were compared with those from particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and evolution strategy (EA), and indicated the higher performance of GWO. Saremi et al. (2015) coupled GWO with the evolutionary population dynamic (EPD) to improve the performance of the basic GWO H. Rezaei O. Bozorg-Haddad (&) Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, 31587-77871 Karaj, Tehran, Iran e-mail: [email protected] H. Rezaei e-mail: [email protected] X. Chu Department of Civil and Environmental Engineering, North Dakota State University, Dept 2470, Fargo, ND 58108-6050, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 O. Bozorg-Haddad (ed.), Advanced Optimization by Nature-Inspired Algorithms, Studies in Computational Intelligence 720, DOI 10.1007/978-981-10-5221-7_9
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algorithm by removing weak individuals from the society. Comparison with the basic GWO illustrated that the proposed algorithm had a better performance in conversion rate and exploration, and also avoided trapping into local optima. Sulaiman et al. (2015) used GWO to solve an optimal reactive power dispatch (ORPD) problem and compared with swarm intelligence (SI), evolutionary computation (EC), PSO, harmony search algorithm (HAS), gravity search algorithm (GSA), invasive weed optimization, and modified imperialist competitive algorithm with invasive weed optimization (MICA-IWO). The results demonstrated that GWO had more desirable optimal solution than others.
9.2
Natural Process of the GWO Algorithm
GWO is inspired by social hierarchy and the intelligent hunting method of grey wolves. Usually, grey wolves are at the top of the food chain in their life areas. Grey wolves mostly live in a pack of 5–12 individuals. In particular, in grey wolves’ life there is a strict social hierarchy. As shown in Fig. 9.1, the leaders of a pack of grey wolves (alpha) are a male a
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