Results 1 to 10 of about 39,754 (281)

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems [PDF]

Scientific Reports
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering.
Manoharan Premkumar, Garima Sinha, Manjula Devi Ramasamy, Santhoshini Sahu, Chithirala Bala Subramanyam, Ravichandran Sowmya, Laith Abualigah, Bizuwork Derebew   +7 more
doaj   +4 more sources

Grey Wolf Optimizer [PDF]

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, Seyed Mohammad Mirjalili, Andrew Lewis 0004   +2 more
semanticscholar   +4 more sources

Adaptive grey wolf optimizer

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, AmirPouya Hemmasian, Seyedali Mirjalili, Amir Barati Farimani   +3 more
semanticscholar   +4 more sources

CMGWO: Grey wolf optimizer for fusion cell-like P systems [PDF]

Heliyon
The grey wolf optimizer is a widely used parametric optimization algorithm. It is affected by the structure and rank of grey wolves and is prone to falling into the local optimum.
Yourui Huang, Quanzeng Liu, Hongping Song, Tao Han, Tingting Li   +4 more
doaj   +3 more sources

A hybrid algorithm of grey wolf optimizer and harris hawks optimization for solving global optimization problems with improved convergence performance

Scientific Reports, 2023
The grey wolf optimizer is an effective and well-known meta-heuristic algorithm, but it also has the weaknesses of insufficient population diversity, falling into local optimal solutions easily, and unsatisfactory convergence speed. Therefore, we propose
Binbin Tu, Fei Wang, Yan Huo, Xiaotian Wang   +3 more
doaj   +2 more sources

A novel fault diagnosis method for gearbox based on RVMD and TELM with composite chaotic grey wolf optimizer [PDF]

Scientific Reports
Fault diagnosis for gearbox by robust variational mode decomposition (RVMD) and twin extreme learning machine (TELM) with composite chaotic grey wolf optimizer (CCGWO) is proposed in this study. Robust variational mode decomposition is an advanced signal
Xuebin Huang, Anfeng Xu, Hongbing Liu, Bingcheng Ye   +3 more
doaj   +2 more sources

A Hybrid Deep Learning and Machine Learning Approach with Mobile-EfficientNet and Grey Wolf Optimizer for Lung and Colon Cancer Histopathology Classification. [PDF]

Cancers (Basel)
Simple Summary Lung and colon cancers are among the leading causes of death globally. Accurate and early detection is essential for improving patient outcomes.
Ochoa-Ornelas R, Gudiño-Ochoa A, García-Rodríguez JA.   +2 more
europepmc   +2 more sources

S-shaped grey wolf optimizer-based FOX algorithm for feature selection. [PDF]

Heliyon
The FOX algorithm is a recently developed metaheuristic approach inspired by the behavior of foxes in their natural habitat. While the FOX algorithm exhibits commendable performance, its basic version, in complex problem scenarios, may become trapped in ...
Feda AK, Adegboye M, Adegboye OR, Agyekum EB, Fendzi Mbasso W, Kamel S.   +5 more
europepmc   +2 more sources

Improved Alpha-Guided Grey Wolf Optimizer [PDF]

IEEE Access, 2019
Grey wolf optimizer (GWO) is a new meta-heuristic swarm intelligence algorithm, which has shown promising performance in solving optimization problems. In order to improve the convergence speed of GWO, an alpha-guided GWO (AgGWO), in which the evolving ...
Pin Hu, Siyi Chen, Huixian Huang, Guangyan Zhang, Lian Liu   +4 more
doaj   +2 more sources

Grey wolf optimizer with self-repulsion strategy for feature selection [PDF]

Scientific Reports
Feature selection is one of the most critical steps in big data analysis. Accurately extracting correct features from massive data can effectively improve the accuracy of big data processing algorithms.
Yufeng Wang, Yumeng Yin, Hang Zhao, Jinxuan Liu, Chunyu Xu, Wenyong Dong   +5 more
doaj   +2 more sources