Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

An Improved Artificial Bee Colony using Cultural Algorithm for Optimization Problem

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Busayo Hadir Adebiyi, Abdoulie Momodou Sunkary Tekanyi, Tijani Ahmed Salawudeen
10.5120/ijca2017913030

Busayo Hadir Adebiyi, Abdoulie Momodou Sunkary Tekanyi and Tijani Ahmed Salawudeen. An Improved Artificial Bee Colony using Cultural Algorithm for Optimization Problem. International Journal of Computer Applications 160(8):14-18, February 2017. BibTeX

@article{10.5120/ijca2017913030,
	author = {Busayo Hadir Adebiyi and Abdoulie Momodou Sunkary Tekanyi and Tijani Ahmed Salawudeen},
	title = {An Improved Artificial Bee Colony using Cultural Algorithm for Optimization Problem},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {8},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {14-18},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume160/number8/27093-2017913030},
	doi = {10.5120/ijca2017913030},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Artificial Bee Colony (ABC) algorithm is a global optimization algorithm which is motivated by the foraging behaviour of swarm of honey bee. The ABC has been successfully employed in solving many kind of complex engineering design problem. But due to the lack of imbalance between exploration and exploitation and insufficient guiding parameters ABC usually get stock into local minima. In order to address this shortcoming a proper guiding parameter which has the ability to change dynamically depending on the nature of problem needs to be introduced. Therefore, this paper proposes an improved ABC algorithm using knowledge inherent in Cultural Algorithm (CA). Two new variants of ABC were developed using situation and normative knowledge. The performance of the developed variants was evaluated using a total of twenty (20) applied mathematical optimization benchmark functions. Simulation results clearly show that, all the newly proposed CABCA variants performed much better than the ABC.

References

  1. Karaboga, D. and B. Basturk, On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 2008. 8(1): p. 687-697.
  2. Hsieh, T.-J., H.-F. Hsiao, and W.-C. Yeh, Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied soft computing, 2011. 11(2): p. 2510-2525.
  3. Gao, W. and S. Liu, Improved artificial bee colony algorithm for global optimization. Information Processing Letters, 2011. 111(17): p. 871-882.
  4. Karaboga, D., An idea based on honey bee swarm for numerical optimization. 2005, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
  5. Shapla, S.S., H. Haque, and M.S. Alam, Explorative Artificial Bee Colony Algorithm: A Novel Swarm Intelligence Based Algorithm For Continuous Function Optimization. International Journal of Science and Research (IJSR), 2015. 4(7).
  6. Lee, W.-P. and W.-T. Cai. A novel artificial bee colony algorithm with diversity strategy. in Natural Computation (ICNC), 2011 Seventh International Conference on. 2011. IEEE.
  7. Sulaiman, N., J. Mohamad-Saleh, and A.G. Abro. A modified artificial bee colony (JA-ABC) optimization algorithm. in Proceedings of the International Conference on Applied Mathematics and Computational Methods in Engineering. 2013.
  8. Banharnsakun, A., T. Achalakul, and B. Sirinaovakul, The best-so-far selection in artificial bee colony algorithm. Applied Soft Computing, 2011. 11(2): p. 2888-2901.
  9. Yan, G. and C. Li, An effective refinement artificial bee colony optimization algorithm based on chaotic search and application for pid control tuning. Journal of Computational Information Systems, 2011. 7(9): p. 3309-3316.
  10. Alam, M.S., M.M. Islam, and K. Murase, Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization.
  11. Reynolds, R.G. and C. Chung. Fuzzy approaches to acquiring experimental knowledge in cultural algorithms. in Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International Conference on. 1997. IEEE.
  12. Reynolds, R.G. and B. Peng. Cultural algorithms: modeling of how cultures learn to solve problems. in Tools with Artificial Intelligence, 2004. ICTAI 2004. 16th IEEE International Conference on. 2004. IEEE.
  13. 13. Reynolds, R.G. and B. Peng, Cultural algorithms: computational modeling of how cultures learn to solve problems: an engineering example. Cybernetics and Systems: An International Journal, 2005. 36(8): p. 753-771.
  14. Chung, C.-J., Knowledge-based approaches to self-adaptation in cultural algorithms. 1997.
  15. Salawudeen, A.T., Development of an Improved Cultural Artificial Fish Swarm Algorithm with Crossover. 2015.
  16. El-Telbany, M.E., Tuning PID controller for DC motor: An artificial bees optimization approach. International Journal of Computer Applications, 2013. 77(15).
  17. Ozen, A. and C. Ozturk. A novel modulation recognition technique based on artificial bee colony algorithm in the presence of multipath fading channels. in Telecommunications and Signal Processing (TSP), 2013 36th International Conference on. 2013. IEEE.
  18. Akay, B. and D. Karaboga, Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 2012. 23(4): p. 1001-1014

Keywords

Artificial Bee Colony, Cultural Algorithm, Exploration, Exploitation, variants, optimization and convergence.