A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification
The rapid growth of data quantity directly leads to the increasing feature dimension, which challenges machine learning and data mining. Wrapper-based intelligent swarm algorithms are effective solution techniques. The Grey Wolf Optimization (GWO) algorithm is a novel intelligent population algorith...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10466551/ |
| _version_ | 1797243328592347136 |
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| author | Li Zhang Xiaobo Chen |
| author_facet | Li Zhang Xiaobo Chen |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | The rapid growth of data quantity directly leads to the increasing feature dimension, which challenges machine learning and data mining. Wrapper-based intelligent swarm algorithms are effective solution techniques. The Grey Wolf Optimization (GWO) algorithm is a novel intelligent population algorithm. Simple principles and few parameters characterize it. However, the basic GWO has disadvantages, such as difficulty coordinating exploration and exploitation capabilities and premature convergence. As a result, GWO fails to identify many irrelevant and redundant features. To improve the performance of the basic GWO algorithm, this paper proposes a velocity-guided grey wolf optimization algorithm with adaptive weights and Laplace operators (VGWO-AWLO). Firstly, by introducing a uniformly distributed dynamic adaptive weighting mechanism, the control parameters <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> are guided to undergo nonlinear dynamic changes to achieve a good transition from the exploratory phase to the development phase. Second, a velocity-based position update formula is designed with an individual memory function to enhance the local search capability of individual grey wolves and drive them to converge to the optimal solution. Thirdly, a Laplace cross-operator strategy is applied to increase the population diversity and help the grey wolf population escape from the local optimal solution. Finally, the VGWO-AWLO algorithm is evaluated for its comprehensive performance in terms of classification accuracy, dimensionality approximation, convergence, and stability in 18 classified datasets. The experimental results show that the classification accuracy and convergence speed of VGWO-AWLO are better than the basic GWO, GWO variants, and other state-of-the-art meta-heuristic algorithms. |
| first_indexed | 2024-04-24T18:53:22Z |
| format | Article |
| id | doaj.art-c7e153cfa189469ba82ec2eeeb4fe189 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:53:22Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c7e153cfa189469ba82ec2eeeb4fe1892024-03-26T17:47:49ZengIEEEIEEE Access2169-35362024-01-0112398873990110.1109/ACCESS.2024.337623510466551A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data ClassificationLi Zhang0https://orcid.org/0000-0002-9306-7778Xiaobo Chen1Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Hainan, ChinaKey Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Hainan, ChinaThe rapid growth of data quantity directly leads to the increasing feature dimension, which challenges machine learning and data mining. Wrapper-based intelligent swarm algorithms are effective solution techniques. The Grey Wolf Optimization (GWO) algorithm is a novel intelligent population algorithm. Simple principles and few parameters characterize it. However, the basic GWO has disadvantages, such as difficulty coordinating exploration and exploitation capabilities and premature convergence. As a result, GWO fails to identify many irrelevant and redundant features. To improve the performance of the basic GWO algorithm, this paper proposes a velocity-guided grey wolf optimization algorithm with adaptive weights and Laplace operators (VGWO-AWLO). Firstly, by introducing a uniformly distributed dynamic adaptive weighting mechanism, the control parameters <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> are guided to undergo nonlinear dynamic changes to achieve a good transition from the exploratory phase to the development phase. Second, a velocity-based position update formula is designed with an individual memory function to enhance the local search capability of individual grey wolves and drive them to converge to the optimal solution. Thirdly, a Laplace cross-operator strategy is applied to increase the population diversity and help the grey wolf population escape from the local optimal solution. Finally, the VGWO-AWLO algorithm is evaluated for its comprehensive performance in terms of classification accuracy, dimensionality approximation, convergence, and stability in 18 classified datasets. The experimental results show that the classification accuracy and convergence speed of VGWO-AWLO are better than the basic GWO, GWO variants, and other state-of-the-art meta-heuristic algorithms.https://ieeexplore.ieee.org/document/10466551/Grey wolf optimization algorithmfeature selectiondynamic adaptive weighting mechanismvelocity update equation mechanismLaplace operators |
| spellingShingle | Li Zhang Xiaobo Chen A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification IEEE Access Grey wolf optimization algorithm feature selection dynamic adaptive weighting mechanism velocity update equation mechanism Laplace operators |
| title | A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification |
| title_full | A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification |
| title_fullStr | A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification |
| title_full_unstemmed | A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification |
| title_short | A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification |
| title_sort | velocity guided grey wolf optimization algorithm with adaptive weights and laplace operators for feature selection in data classification |
| topic | Grey wolf optimization algorithm feature selection dynamic adaptive weighting mechanism velocity update equation mechanism Laplace operators |
| url | https://ieeexplore.ieee.org/document/10466551/ |
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