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A chaotic grey wolf optimizer for constrained optimization problems
Expert Systems, 2021AbstractBio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack.
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A better exploration strategy in Grey Wolf Optimizer
Journal of Ambient Intelligence and Humanized Computing, 2020The Grey Wolf Optimizer (GWO) is a recently developed population-based meta-heuristics algorithm that mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Although, GWO has shown very good results on several real-life applications but still it suffers from some issues like, the low exploration and slow convergence rate ...
Jagdish Chand Bansal, Shitu Singh
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Recommender system with grey wolf optimizer and FCM
Neural Computing and Applications, 2016Recommender systems are contributing a significant aspect in information filtering and knowledge management systems. They provide explicit and reliable recommendations to the users so that user can get information about all products in e-commerce domain.
Rahul Katarya, Om Prakash Verma
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A novel Random Walk Grey Wolf Optimizer
Swarm and Evolutionary Computation, 2019Abstract Grey Wolf Optimizer (GWO) algorithm is a relatively new algorithm in the field of swarm intelligence for solving continuous optimization problems as well as real world optimization problems. The Grey Wolf Optimizer is the only algorithm in the category of swam intelligence which is based on leadership hierarchy.
Shubham Gupta, Kusum Deep
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LGWO: An Improved Grey Wolf Optimization for Function Optimization
2017Grey wolf optimization (GWO) algorithm is a novel nature-inspired heuristic paradigm. GWO was inspired by grey wolves, which mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. It has exhibited promising performance in many fields. However, GWO algorithm has the drawback of slow convergence and low precision.
Jie Luo 0002 +5 more
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Grey wolf optimizer with cellular topological structure
Expert Systems with Applications, 2018Abstract Grey wolf optimizer (GWO) is a newly developed metaheuristic inspired by hunting mechanism of grey wolves. The paramount challenge in GWO is that it is prone to stagnation in local optima. This paper proposes a cellular grey wolf optimizer with a topological structure (CGWO). The proposed CGWO has two characteristics.
Chao Lu 0008, Liang Gao 0001, Jin Yi
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Anais do XVI Congresso Brasileiro de Inteligência Computacional
Swarm-based metaheuristics have become the most prominent method for solving optimization problems. Several operators already proposed in the literature can also be reused to expand the current metaheuristics. We present in this paper the Volitive Grey Wolf Optimizer (VGWO), a Grey Wolf Optimizer variant created by the addition of the collective ...
Joao Paulo Silva +4 more
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Swarm-based metaheuristics have become the most prominent method for solving optimization problems. Several operators already proposed in the literature can also be reused to expand the current metaheuristics. We present in this paper the Volitive Grey Wolf Optimizer (VGWO), a Grey Wolf Optimizer variant created by the addition of the collective ...
Joao Paulo Silva +4 more
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Hybrid Grey Wolf Optimizer with Mutation Operator
2018Grey Wolf Optimizer (GWO), developed by Mirjalili et al. (Adv Eng Softw 69:46–61, 2014 [1]), is a recently developed nature-inspired technique based on leadership hierarchy of grey wolves. In this paper, Grey Wolf Optimizer has been hybridized with differential evolution (DE) mutation, and two versions, namely DE-GWO and gDE-GWO, have been proposed to ...
Shubham Gupta, Kusum Deep
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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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Boolean Binary Grey Wolf Optimizer
2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2022Rodrigo Cesar Lira +3 more
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