Skip to main content


Log in

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

  • Original Paper
  • Published:
Journal of Global Optimization Aims and scope Submit manuscript


Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  1. Pham D.T., Karaboga D. (2000). Intelligent Optimisation Techniques. Springer, London

    Google Scholar 

  2. Holland J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  3. De Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems. Part I. Basic Theory And Applications. Technical Report No. Rt Dca 01/99, Feec/Unicamp, Brazil (1999)

  4. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE Service Center, Piscat away (1995)

  5. Fukuyama, Y., Takayama, S., Nakanishi, Y., Yoshida, H.: A particle swarm optimization for reactive power and voltage control in electric power systems. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1523–1528. Orlando, Florida, USA (1999)

  6. Tereshko V. (2000). Reaction-diffusion model of a honeybee colony’s foraging behaviour. In: Schoenauer, M. (eds) Parallel Problem Solving from Nature VI. Lecture Notes in Computer Science, pp 807–816. Springer, Berlin

    Chapter  Google Scholar 

  7. Tereshko V., Lee T. (2002). How information mapping patterns determine foraging behaviour of a honey bee colony. Open Syst. Inf. Dyn. 9: 181–193

    Article  Google Scholar 

  8. Tereshko V., Loengarov A. (2005). Collective Decision-Making in Honey Bee Foraging Dynamics. Comput. Inf. Sys. J. 9(3): 1–7

    Google Scholar 

  9. Teodorović D. (2003). Transport Modeling By Multi-Agent Systems: A Swarm Intellgence Approach, Transport. Plan. Technol. 26(4): 289–312

    Article  Google Scholar 

  10. Lucic, P., Teodorović, D.: Transportation Modeling: An Artificial Life Approach. ICTAI, pp. 216–223. Washington D.C. (2002)

  11. Teodorović, D., Dell’Orco, M.: Bee colony optimization—a cooperative learning approach to complex transportation problems. In: Proceedings of the 10th EWGT Meeting, Poznan, 13–16 September 2005

  12. Drias, H., Sadeg, S., Yahi, S.: Cooperative bees swarm for solving the maximum weighted satisfiability problem, computational intelligence and bioinspired Systems. In: Proceedings of the 8th International Workshop on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltr, Barcelona, Spain, 8–10 June 2005

  13. Benatchba, K., Admane, L., Koudil, M.: Using bees to solve a data-mining problem expressed as a max-sat one, artificial intelligence and knowledge engineering applications: a bioinspired approach. In: Proceedings of the First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, 15–18 June 2005

  14. Wedde, H.F., Farooq, M., Zhang, Y.: BeeHive: an efficient fault-tolerant routing algorithm inspired by honey bee behavior, ant colony, optimization and swarm intelligence. In: Proceedings of the 4th International Workshop, ANTS 2004, Brussels, Belgium, 5–8 September 2004

  15. Yang, X.S.: Engineering optimizations via nature-inspired virtual bee algorithms. Lecture Notes in Computer Science, pp. 317–323. Springer, GmbH (2005)

  16. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005

  17. Basturk, B., Karaboga, D.: An artificial bee colony (ABC) algorithm for numeric function optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA, 12–14 May 2006

  18. Hadley G. (1964). Nonlinear and Dynamics Programming. Addison Wesley, Reading, MA

    Google Scholar 

  19. Boyer, D.O., Martnez, C.H., Pedrajas, N.G.: Crossover Operator for Evolutionary Algorithms Based on Population Features.

  20. Friedman, J.H.: An overview of predictive learning and function approximation. From Statistics to Neural Networks, Theory and Pattern Recognition Applications, NATO ASI Series F, vol. 136, pp. 1–61. Springer, Berlin (1994)

  21. Srinivasan, D., Seow, T.H.: Evolutionary Computation, CEC ’03, 8–12 Dec. 2003, 4(2003), Canberra, Australia, pp. 2292–2297.


Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Bahriye Basturk.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Karaboga, D., Basturk, B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39, 459–471 (2007).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: