Soft Computing

, Volume 22, Issue 9, pp 2935–2952 | Cite as

A self-adaptive artificial bee colony algorithm based on global best for global optimization

  • Yu XueEmail author
  • Jiongming Jiang
  • Binping Zhao
  • Tinghuai Ma
Methodologies and Application


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.


Artificial 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.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Agrawal SK, Sahu OP (2015) Artificial bee colony algorithm to design two-channel quadrature mirror filter banks. Swarm Evol Comput 21:24–31CrossRefGoogle Scholar
  2. Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37(8):5682–5687CrossRefGoogle Scholar
  3. Al-Salamah M (2015) Constrained binary artificial bee colony to minimize the make span for single machine batch processing with non-identical job sizes. Appl Soft Comput 29(C):379–385CrossRefGoogle Scholar
  4. Babaoglu I (2015) Artificial bee colony algorithm with distribution-based update rule. Appl Soft Comput 34:851–861CrossRefGoogle Scholar
  5. Bansal JC, Sharma H, Arya KV, Nagar A (2013) Memetic search in artificial bee colony algorithm. Soft Comput 17(10):1911–1928CrossRefGoogle Scholar
  6. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRefGoogle Scholar
  7. Epitropakis MG, Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evol Comput 15(1):99–119CrossRefGoogle Scholar
  8. Frank A, Asunction A (2010) UCI machine learning repository.
  9. Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697CrossRefzbMATHGoogle Scholar
  10. Gu B, Sheng VS (2013) Feasibility and finite convergence analysis for accurate on-line-support vector machine. IEEE Trans Neural Netw Learn Syst 24(8):1304–1315CrossRefGoogle Scholar
  11. Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753MathSciNetCrossRefzbMATHGoogle Scholar
  12. Gao WF, Liu SY, Huang LL (2013) A novel artificial bee colony algorithm with Powell’s method. Appl Soft Comput 13(9):3763–3775CrossRefGoogle Scholar
  13. Gao WF, Liu SY, Huang LL (2014) Enhancing artificial bee colony algorithm using more information-based search equations. Inf Sci 270:112–133MathSciNetCrossRefzbMATHGoogle Scholar
  14. 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
  15. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, CambridgezbMATHGoogle Scholar
  16. Horng SC (2015) Combining artificial bee colony with ordinal optimization for stochastic economic lot scheduling problem. IEEE Trans Syst Man Cybern Syst 45(3):373–384CrossRefGoogle Scholar
  17. Kang F, Li JJ, Li HJ (2013) Artificial bee colony algorithm and pattern search hybridized for global optimization. Appl Soft Comput 13(4):1781–1791. doi: 10.1016/j.asoc.2012.12.025 CrossRefGoogle Scholar
  18. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetCrossRefzbMATHGoogle Scholar
  19. Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948Google Scholar
  20. 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
  21. Li X, Yin M (2014) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl 24(3):723–734MathSciNetCrossRefGoogle Scholar
  22. Li JQ, Pan QK (2015) Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm. Inf Sci 316:487–502CrossRefGoogle Scholar
  23. Liu TT, Zhang CS, Zhang B, Sun RN (2015) A strategy self-adaptive selection bee colony algorithm based on feedback. J Northeast Univ 5(3):618–630zbMATHGoogle Scholar
  24. 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
  25. Rahnamayan S, Tizhoosh HR, Salama MA (2008) Opposition-based differential evolution. IEEE Trans Evolut Comput 12(1):64–79CrossRefGoogle Scholar
  26. Rajasekhar A, Pant M (2014) An improved self-adaptive artificial bee colony algorithm for global optimisation. Int J Swarm Intell 1(2):115–132CrossRefGoogle Scholar
  27. Roy R, Sevick-Muraca EM (1999) Truncated Newton’s optimization scheme for absorption and fluorescence optical tomography: part I theory and formulation. Opt Express 4(10):353–371CrossRefGoogle Scholar
  28. Setiono R, Hui LK (1995) Use of a quasi-newton method in a feedforward neural network construction algorithm. IEEE Trans Neural Netw 6(1):273–277CrossRefGoogle Scholar
  29. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefzbMATHGoogle Scholar
  30. 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
  31. Wen X, Shao L, Fang W, Xue Y (2015) Efficient feature selection and classification for vehicle detection. IEEE Trans Circuits Syst Video Technol 25(3):508–517CrossRefGoogle Scholar
  32. Xia F, Liu L, Li J, Ahmed AM, Yang LT, Ma J (2015) Beeinfo: interest-based forwarding using artificial bee colony for socially aware networking. IEEE Trans Veh Technol 64(3):1188–1200CrossRefGoogle Scholar
  33. Yi J, Gao L, Li X, Gao J (2016) An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems. Appl Intell 44(3):725–753CrossRefGoogle Scholar
  34. Zhang X, Zhang X, Ho SL, Fu WN (2014) A modification of artificial bee colony algorithm applied to loudspeaker design problem. IEEE Trans Magn 50(2):737–740CrossRefGoogle Scholar
  35. Zhu GP, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Yu Xue
    • 1
    • 2
    Email author
  • Jiongming Jiang
    • 1
  • Binping Zhao
    • 1
  • Tinghuai Ma
    • 1
  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjingChina

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