A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm
Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving co...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2010/459796 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849692030852136960 |
|---|---|
| author | Wenping Zou Yunlong Zhu Hanning Chen Xin Sui |
| author_facet | Wenping Zou Yunlong Zhu Hanning Chen Xin Sui |
| author_sort | Wenping Zou |
| collection | DOAJ |
| description | Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed. |
| format | Article |
| id | doaj-art-b09c1155ffbc4c8fac0ae9f48391f972 |
| institution | DOAJ |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-b09c1155ffbc4c8fac0ae9f48391f9722025-08-20T03:20:50ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2010-01-01201010.1155/2010/459796459796A Clustering Approach Using Cooperative Artificial Bee Colony AlgorithmWenping Zou0Yunlong Zhu1Hanning Chen2Xin Sui3Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaArtificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.http://dx.doi.org/10.1155/2010/459796 |
| spellingShingle | Wenping Zou Yunlong Zhu Hanning Chen Xin Sui A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm Discrete Dynamics in Nature and Society |
| title | A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm |
| title_full | A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm |
| title_fullStr | A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm |
| title_full_unstemmed | A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm |
| title_short | A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm |
| title_sort | clustering approach using cooperative artificial bee colony algorithm |
| url | http://dx.doi.org/10.1155/2010/459796 |
| work_keys_str_mv | AT wenpingzou aclusteringapproachusingcooperativeartificialbeecolonyalgorithm AT yunlongzhu aclusteringapproachusingcooperativeartificialbeecolonyalgorithm AT hanningchen aclusteringapproachusingcooperativeartificialbeecolonyalgorithm AT xinsui aclusteringapproachusingcooperativeartificialbeecolonyalgorithm AT wenpingzou clusteringapproachusingcooperativeartificialbeecolonyalgorithm AT yunlongzhu clusteringapproachusingcooperativeartificialbeecolonyalgorithm AT hanningchen clusteringapproachusingcooperativeartificialbeecolonyalgorithm AT xinsui clusteringapproachusingcooperativeartificialbeecolonyalgorithm |