Results 211 to 220 of about 39,754 (281)
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2020
GWO algorithm is a swarm or population-based meta-heuristic technique developed based on motivation from the hunting pattern of the Grey Wolves (GW). In this study, the model was implemented using MATLAB 2020. Thirty (30) search agents were considered and the maximum number of iterations was set to 1000.
Ahmed F. Ali, Mohamed A. Tawhid
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GWO algorithm is a swarm or population-based meta-heuristic technique developed based on motivation from the hunting pattern of the Grey Wolves (GW). In this study, the model was implemented using MATLAB 2020. Thirty (30) search agents were considered and the maximum number of iterations was set to 1000.
Ahmed F. Ali, Mohamed A. Tawhid
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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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IEEE Transactions on Automation Science and Engineering, 2022
Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy.
Xiwang Guo +5 more
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Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy.
Xiwang Guo +5 more
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The Chinese Journal of Artificial Intelligence, 2022
Background: The Particle Swarm Optimization (PSO) algorithm is amongst the utmost favourable optimization algorithms often employed in hybrid procedures by the researchers considering simplicity, smaller count of parameters involved, convergence speed and capability of searching global optima.
Sumita Gulati, Ashok Pal
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Background: The Particle Swarm Optimization (PSO) algorithm is amongst the utmost favourable optimization algorithms often employed in hybrid procedures by the researchers considering simplicity, smaller count of parameters involved, convergence speed and capability of searching global optima.
Sumita Gulati, Ashok Pal
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On the improvement in grey wolf optimization
Neural Computing and Applications, 2019Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation.
Rohit Salgotra +2 more
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Quality and Reliability Engineering International, 2023
Analysis and optimization of system reliability have very much importance for developing an optimal design for the system while using the available resources. Several studies are centered towards reliability optimization using metaheuristics.
Ashok Singh Bhandari +2 more
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Analysis and optimization of system reliability have very much importance for developing an optimal design for the system while using the available resources. Several studies are centered towards reliability optimization using metaheuristics.
Ashok Singh Bhandari +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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Learning-Based Grey Wolf Optimizer for Stochastic Flexible Job Shop Scheduling
IEEE Transactions on Automation Science and Engineering, 2022This work considers a stochastic flexible job shop scheduling with limited extra resources and machine-dependent setup time in a semiconductor manufacturing environment, which is an NP-hard problem.
Chengran Lin, Zhengcai Cao, Mengchu Zhou
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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.
Himani Joshi, Sankalap Arora
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Application of Mutation Operators on Grey Wolf Optimizer
2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021Nature-inspired computing has been widely used for solving different optimization problems. Grey wolf optimization (GWO) is one of the recent addition in the category of swarm-based techniques. The swarm-based algorithms try to find a balance between exploitation and exploration through different steps/processes.
Priyanka Singh 0003, Rahul Kottath
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