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An improved hybrid grey wolf optimization algorithm
Soft Computing, 2018The existing grey wolf optimization algorithm has some disadvantages, such as slow convergence speed, low precision and so on. So this paper proposes a grey wolf optimization algorithm combined with particle swarm optimization (PSO_GWO). In this new algorithm, the Tent chaotic sequence is used to initiate the individuals’ position, which can increase ...
Zhi-jun Teng, Jin-ling Lv, Li-wen Guo
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A fuzzy hierarchical operator in the grey wolf optimizer algorithm
Applied Soft Computing, 2017Abstract The main goal of this paper is to study the performance of the Grey Wolf Optimizer (GWO) algorithm when a new hierarchical operator is introduced in the algorithm. This new operator is basically a hierarchical transformation that is inspired in the hierarchical social pyramid of the grey wolf.
Luis Rodríguez +7 more
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Grey Wolf Optimization (GWO) Algorithm
2017This 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.
Hossein Rezaei +2 more
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A Hybrid Grey Wolf-Bat Algorithm for Global Optimization
2018Bio-inspired algorithms are now becoming powerful methods for solving many real-world optimization problems. In this paper, we propose a hybrid approach involving Grey Wolf optimizer (GWO) and Bat swarm optimizer (BA) for global function optimization problems. GWO is well known for its balanced exploration/exploitation behavior, while BA is known to be
Mohammed ElGayyar +3 more
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A four-step decision-making grey wolf optimization algorithm
Soft Computing, 2021Grey wolf optimization (GWO) is a meta-heuristic inspired by the social hierarchy and the hunting behaviors observed in wolves. The GWO algorithm has been successfully applied in many fields, such as finance, engineering and industry. However, the GWO has the disadvantages that it is prone to stagnate in local solutions, and the convergence may be ...
Chengsheng Pan +3 more
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Modified Grey Wolf Algorithm for optimization problems
2016 International Conference on Inventive Computation Technologies (ICICT), 2016Grey Wolf Algorithm (GWA) is the recently developed metaheuristic technique. It is inspired from wolf's social and hunting behavior. In paper, a modification in GWA is proposed to provide the better balance between exploration and exploitation. The proposed algorithm is tested on thirteen benchmark test functions.
null Seema, Vijay Kumar
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An improved grey wolf algorithm for global optimization
2018 Chinese Control And Decision Conference (CCDC), 2018This paper presents a novel improved grey wolf optimization (IGWO) to avoid local minimization and premature convergence. In the proposed IGWO algorithm, a nonlinear control parameter based on cosine function is presented to ensure a faster convergence rate of late iteration.
Wendong Gai +3 more
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Fully Informed Grey Wolf Optimizer Algorithm
2020Grey wolf optimizer (GWO) is a newly generated metaheuristic search algorithm inspired by the social behaviour of the grey wolf, which resembles the social structure and hunting mechanism of grey wolves in nature, and is based on three main steps: searching for prey, encircling prey and attacking prey. This paper presents a new variant of GWO algorithm
Priyanka Meiwal +2 more
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Multidirectional Grey Wolf Optimizer Algorithm for Solving Global Optimization Problems
International Journal of Computational Intelligence and Applications, 2018In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm.
Mohamed A. Tawhid, Ahmed Fouad Ali
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Effects of dominant wolves in grey wolf optimization algorithm
Applied Soft Computing, 2019Abstract Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements.
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