ISSN 2456-0235

International Journal of Modern Science and Technology

INDEXED IN 

​​​​International Journal of Modern Science and Technology, 1(9), 2016, Pages 304-310. 


Optimization Using Artificial Bee Colony Algorithm

R. Subashini, A. R. Rishivarman
Department of Mathematics, Theivanai Ammal College for Women (Autonomous) Villupuram - 605 401. Tamilnadu, India.

Abstract
Swarm intelligence is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by swarm intelligence are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony is the one which has been most widely studied on and applied to solve the real world problems. Real-world optimization problems are very difficult and have high degrees of uncertainty. Artificial bee colony algorithm has proved its importance in solving a number of problems including engineering optimization problems. Artificial bee colony algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. The Artificial bee colony algorithm for solving non-linear problems is presented in the present review.

​​

Keywords: Artificial bee colony algorithm; Meta-heuristic algorithms; Swarm intelligence; Optimization technique.


​References

  1. Karaboga D, Gorkemli B, Ozturk C, Karaboga N. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review. 2014:42:21-57.
  2. Karaboga D. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation. 2009;214:108-132.
  3. Karaboga D, Basturk B. On the Performance of Artificial Bee Colony (ABC) Algorithm. Applied Soft Computing. 2008;8:687-697.
  4. Karaboga D, Basturk B. Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. P. Melin et al. (Eds.): Berlin Heidelberg: Springer-Verlag; 2007:789-798.
  5. Bonabeau E, Dorigo M, Theraulaz, G. Swarm intelligence-from natural to artificial systems. Oxford: Oxford University Press:1999.
  6. Millonas MM. Swarms, phase transitions, and collective intelligence. In: Artificial Life III. (C. G. Langton, Ed.,). Addison Wesley, Reading, MA:1994.
  7. Neelima S, Satyanarayana N, Krishna Murthy P. A Comprehensive Survey on Variants in Artificial Bee Colony (ABC).2016;7(4):1684-1689.
  8. Fahad S, Mouti A, El-Hawary ME. Overview of Artificial Bee Colony (ABC) Algorithm and Its Applications. IEEE. 2012.
  9. Kumar S, Sharma VK, Kumari R. A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem. International Journal of Computer Applications. 2013;82:18-25.
  10. Sehra SS. A Systematic Review of Applications of Bee Colony Optimization. Proceedings of the International Conference on Innovation and Challenges in Cyber Security. 2016:257-260. DOI:10.1109/ICICCS.2016.7542297.
  11. Wang Y, Li Y. Multi-objective Artificial Bee Colony algorithm. Swarm and Evolutionary Computation. 2015;2:39-52.
  12. Harfouchi F, Habbi H. A Cooperative Learning Strategy with Multiple Search Mechanisms for Improved ABC. Proceedings of the International Conference on Intelligent Systems Design and Applications 2015. DOI:10.1109/ISDA.2015.7489269.
  13. Baykasoglu A, Ozbakir L, Tapkan P. Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem. In: Swarm intelligence focus on ant and particle swarm optimization. (Felix T.S. Chan and Manoj Kumar Tiwari, Eds) 2007:113-144.
  14. Quan H and Shi X. On the analysis of performance of the improved artificial-bee colony algorithm. Proceedings of the International Conference on Natural Computation. 2008:654-658. DOI:10.1109/ICNC.2008.211.
  15. Kennedy J, Eberhart R, Shi Y. Swarm Intelligence. Morgan-Kaufmann Publishers Inc. San Francisco, CA, USA:2001.
  16. Sharma TK, Pant M. Golden Search based Artificial Bee Colony Algorithm and its Application to Solve Engineering Design Problems. Proceedings of the Advanced Computing and Communication Technologies. 2012:156-160. DOI:10.1109/ACCT.2012.59.
  17. Sharma TK, Pant M, Singh VP. Improved Local Search in Artificial Bee Colony using Golden Section Search. Journal of Engineering. 1;2012:1-6.