Improved Honey Badger Algorithm and Its Application to K-Means Clustering
As big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial clustering center of mass. In this paper, an improv...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/718 |
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| author | Shuhao Jiang Huimin Gao Yizi Lu Haoran Song Yong Zhang Mengqian Wang |
| author_facet | Shuhao Jiang Huimin Gao Yizi Lu Haoran Song Yong Zhang Mengqian Wang |
| author_sort | Shuhao Jiang |
| collection | DOAJ |
| description | As big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial clustering center of mass. In this paper, an improved honey badger optimization algorithm is proposed: (1) The population is initialized using sin chaos to make the population uniformly distributed. (2) The density factor is improved to enhance the optimization accuracy of the population. (3) A nonlinear inertia weight factor is introduced to prevent honey badger individuals from relying on the behavior of past individuals during position updating. (4) To improve the diversity of solutions, random opposition learning is performed on the optimal individuals. The improved algorithm outperforms the comparison algorithm in terms of performance through experiments on 23 benchmark test functions. Finally, in this paper, the improved algorithm is applied to K-means clustering and experiments are conducted on three data sets from the UCI data set. The results show that the improved honey badger optimized K-means algorithm improves the clustering effect over the traditional K-means algorithm. |
| format | Article |
| id | doaj-art-4168b3025ef2474d99153dd08d4b5623 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4168b3025ef2474d99153dd08d4b56232025-01-24T13:20:35ZengMDPI AGApplied Sciences2076-34172025-01-0115271810.3390/app15020718Improved Honey Badger Algorithm and Its Application to K-Means ClusteringShuhao Jiang0Huimin Gao1Yizi Lu2Haoran Song3Yong Zhang4Mengqian Wang5School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Science, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaAs big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial clustering center of mass. In this paper, an improved honey badger optimization algorithm is proposed: (1) The population is initialized using sin chaos to make the population uniformly distributed. (2) The density factor is improved to enhance the optimization accuracy of the population. (3) A nonlinear inertia weight factor is introduced to prevent honey badger individuals from relying on the behavior of past individuals during position updating. (4) To improve the diversity of solutions, random opposition learning is performed on the optimal individuals. The improved algorithm outperforms the comparison algorithm in terms of performance through experiments on 23 benchmark test functions. Finally, in this paper, the improved algorithm is applied to K-means clustering and experiments are conducted on three data sets from the UCI data set. The results show that the improved honey badger optimized K-means algorithm improves the clustering effect over the traditional K-means algorithm.https://www.mdpi.com/2076-3417/15/2/718HBAK-meanssin chaosnonlinear density factornonlinear inertia weightsrandom opposition-based learning |
| spellingShingle | Shuhao Jiang Huimin Gao Yizi Lu Haoran Song Yong Zhang Mengqian Wang Improved Honey Badger Algorithm and Its Application to K-Means Clustering Applied Sciences HBA K-means sin chaos nonlinear density factor nonlinear inertia weights random opposition-based learning |
| title | Improved Honey Badger Algorithm and Its Application to K-Means Clustering |
| title_full | Improved Honey Badger Algorithm and Its Application to K-Means Clustering |
| title_fullStr | Improved Honey Badger Algorithm and Its Application to K-Means Clustering |
| title_full_unstemmed | Improved Honey Badger Algorithm and Its Application to K-Means Clustering |
| title_short | Improved Honey Badger Algorithm and Its Application to K-Means Clustering |
| title_sort | improved honey badger algorithm and its application to k means clustering |
| topic | HBA K-means sin chaos nonlinear density factor nonlinear inertia weights random opposition-based learning |
| url | https://www.mdpi.com/2076-3417/15/2/718 |
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