A self-adaptive artificial bee colony algorithm based on global best for global optimization
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Intelligent optimization algorithms based on evolutionary and swarm principles have been widely researched in recent years. The artificial bee colony (ABC) algorithm is an intelligent swarm algorithm for global optimization problems. Previous studies have shown that the ABC algorithm is an efficient, effective, and robust optimization method. However, the solution search equation used in ABC is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although some complex strategies for generating candidate solutions have recently been developed, the universality and robustness of these new algorithms are still insufficient. This is mainly because only one strategy is adopted in the modified ABC algorithm. In this paper, we propose a self-adaptive ABC algorithm based on the global best candidate (SABC-GB) for global optimization. Experiments are conducted on a set of 25 benchmark functions. To ensure a fair comparison with other algorithms, we employ the same initial population for all algorithms on each benchmark function. Besides, to validate the feasibility of SABC-GB in real-world application, we demonstrate its application to a real clustering problem based on the K-means technique. The results demonstrate that SABC-GB is superior to the other algorithms for solving complex optimization problems. It means that it is a new technique to improve the ABC by introducing self-adaptive mechanism.
KeywordsArtificial bee colony (ABC) Global optimization Search strategy Self-adaptive
This study was funded by National Natural Science Foundation of China (Grant Number 61403206), by Natural Science Foundation of Jiangsu Province (Grant Number BK20141005), by Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant Number 14KJB520025), by Priority Academic Program Development of Jiangsu Higher Education Institutions.
Compliance with ethical standards
Conflict of interest
Authors Yu Xue , Jiongming Jiang, Binping Zhao and Tinghuai Ma declares that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Frank A, Asunction A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml/datasets.html
- He P, Yan XD, Shi HB (2013) A quick self-adaptive artificial bee colony algorithm and its application. J East China Univ Sci Technol 5:588–595Google Scholar
- Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948Google Scholar
- Ji J, Pang W, Zheng Y, Wang Z, Ma Z (2015) A novel artificial bee colony based clustering algorithm for categorical data. PLoS ONE 10(5):e0127125. doi: 10.1371/journal.pone.0127125
- Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, vol 1, no 14, pp 281–297, University of California Press, BerkeleyGoogle Scholar
- Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Nanyang Technological University, SingaporeGoogle Scholar