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Group-based synchronous-asynchronous Grey Wolf Optimizer
Applied Mathematical Modelling, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alma Rodríguez +7 more
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Chaotic grey wolf optimization
2016 International Conference on Progress in Informatics and Computing (PIC), 2016Grey wolf optimization algorithm (GWO) is a recently proposed meta-heuristics and has shown promising performance in solving complex function optimization and engineering problems. To further enrich the search dynamics of GWO, the chaotic local search (CLS) mechanism is incorporated into GWO to enhance the search by taking the properties of ergodicity ...
Hang Yu +4 more
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Enhanced Grey Wolf Optimization Algorithm for Global Optimization
Fundamenta Informaticae, 2017Grey Wolf Optimizer (GWO) is a new meta-heuristic search algorithm inspired by the social behavior of leadership and the hunting mechanism of grey wolves. GWO algorithm is prominent in terms of finding the optimal solution without getting trapped in premature convergence. In the original GWO, half of the iterations are dedicated to exploration and the
Joshi, Himani, Arora, Sankalap
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Randomized Neighbour Grey Wolf Optimizer
2021At present time, swarm-intelligence-related algorithm covers a wide area of optimization. Grey wolf optimizer (GWO) algorithm is one of them. Grey wolves work in a group and they pantomimist the communal style of the wolf. In GWO a solution is upgraded by consolidating the instruction from the three best solutions in the explore area [1].
Shahnawaz Ali +2 more
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Improved Discrete Grey Wolf Optimizer
2018 26th European Signal Processing Conference (EUSIPCO), 2018Grey wolf optimizer (GWO) is a bioinspired iterative optimization algorithm which simulates the hunting process of a wolf pack guided by three leaders. In this paper, a novel discrete GWO is proposed: a random leader selection is performed, and the probability for the main leader to be selected increases at the detriment of the other leaders across ...
Martin, Benoît +2 more
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Modified Binary Grey Wolf Optimizer
2020This chapter presents applications of the meta-heuristic known as grey wolf optimizer (GWO) to solve the NP-hard transmission network expansion planning (TNEP) problem. The computational intelligence technique known as GWO mathematically models the hunting technique and social hierarchy of grey wolves to solve optimization problems.
Gustavo Rebello +1 more
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Entropy Based Grey Wolf Optimizer
2020Recently Shannon’s Entropy has been incorporated in nature inspired metaheuristics with good results. Depending on the problem, the Grey Wolf Optimization (GWO) algorithm may suffer from premature convergence. Here, an Entropy Grey Wolf Optimization (E-GWO) technique is proposed with the overall aim to improve the original GWO performance.
Daniel Duarte +2 more
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IMPROVEMENT OF WOLF LEADER IN THE GREY WOLF OPTIMIZATION
Konya Journal of Engineering Sciences, 2023The development of optimization algorithms attracts the attention of many analysts as it has advantages such as increasing performance, revenue, and efficiency in various fields, and reducing cost. Swarm-based optimization algorithms, which are among the meta-heuristic methods, are more commonly preferred because they are generally successful.
Onur İNAN, Mustafa Serter UZER
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Repulsion-Based Grey Wolf Optimizer
2020In this paper, a novel algorithm called Repulsion-based Grey Wolf Optimizer (R-GWO) is presented. The proposed algorithm is an improvement of the already existing popular swarm intelligence algorithm, called Grey Wolf Optimizer (GWO). The aim of the proposed work is to enhance the balance between exploitation and exploration in GWO by introducing a ...
Ankita Wadhwa, Manish Kumar Thakur
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Neighbourhood-Inspired Grey Wolf Optimizer
2020Over the past few decades, swarm intelligence (SI)-based algorithms are performing well in the optimization field. Grey wolf optimizer (GWO) is a new addition for the class of SI-based algorithms. It mimes the social behaviour of grey wolves. In GWO, a solution is updated by incorporating the information from the first fittest, second fittest and third
Priyanka Meiwal +2 more
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