Results 11 to 20 of about 13,304 (184)
Advances in Engineering Software, 2014 This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy.
Seyedali Mirjalili +2 more
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Selective Opposition based Grey Wolf Optimization
Expert Systems with Applications, 2020 Abstract The use of metaheuristics is widespread for optimization in both scientific and industrial problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf Optimizer (GWO) is one of the most recent and popular algorithms in this area.
Souvik Dhargupta +3 more
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An integrative TLBO-driven hybrid grey wolf optimizer for the efficient resolution of multi-dimensional, nonlinear engineering problems [PDF]
Scientific ReportsThis research article introduces a hybrid optimization algorithm, referred to as Grey Wolf Optimizer-Teaching Learning Based Optimization (GWO-TLBO), which extends the Grey Wolf Optimizer (GWO) by integrating it with Teaching-Learning-Based Optimization (
Harleenpal Singh +6 more
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Enhanced futures price-spread forecasting based on an attention-driven optimized LSTM network: integrating an improved grey wolf optimizer algorithm for enhanced accuracy [PDF]
PeerJ Computer ScienceFinancial market prediction faces significant challenges due to the complex temporal dependencies and heterogeneous data relationships inherent in futures price-spread data. Traditional machine learning methods struggle to effectively mine these patterns,
Yongli Tang +4 more
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MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer [PDF]
SensorsThe primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions.
Jing Liu +3 more
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IEEE Access, 2021 Conventional fuzzy clustering algorithms present several disadvantages with respect to image segmentation, including a tendency to arrive at local optima and a relatively high sensitivity to noise and initial cluster centers.
Xiangxiao Lei, Honglin Ouyang
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An Improved Grey Wolf Optimization Algorithm and its Application in Path Planning
IEEE Access, 2021 Grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems.
Jingyi Liu, Xiuxi Wei, Huajuan Huang
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Neural Computing and Applications, 2022 Swarm-based metaheuristic optimization algorithms have demonstrated outstanding performance on a wide range of optimization problems in both science and industry. Despite their merits, a major limitation of such techniques originates from non-automated parameter tuning and lack of systematic stopping criteria that typically leads to inefficient use of ...
Kazem Meidani +3 more
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IEEE Access, 2020 Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking.
M. W. Guo +4 more
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Frontiers in Earth Science, 2023 Considering the strong non-linear time-varying behavior of dam deformation, a novel prediction model, called Levy flight-based grey wolf optimizer optimized support vector regression (LGWO-SVR), is proposed to forecast the displacements of hydropower ...
Peng He, Wenjing Wu
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