Abstract
To overcome the drawbacks of artificial bee colony(ABC) algorithm that converges slowly in the process of searching and easily suffers from premature, this paper presents an effective approach, called ABC using opposition-based learning(OBL-ABC). It generates opposite solution by the employed bee and onlooker bee, and chooses the better solution as the new locations of employed bee and onlooker bee according to the greedy selection strategy in order to enlarge the search areas; the new approach proposes a new update rule which can retain the advantages of employed bee and onlooker bee and improve the exploration of OBL-ABC. Experiments are conducted on a set of test functions to verify the performance of OBL-ABC, the results demonstrate promising performance of our method OBL-ABC on convergence and it is suitable for solving the optimization of complex functions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Karaboga, D.: An idea based on honey bee swarn for numerical optimization. Erciyes University, Kayseri (2005)
Weifeng, G., Sanyang, L., Lingling, H.: Inspired Artificial Bee Colony Algorithm for Global Optimization Problems. Acta Electronica Sinica 40(2), 2396–2403 (2014)
Pei-Wei, C., Jeng-Shyang, P., Bin-Yih, L., Shu-Chuan, C.: Enhanced Artificial Bee Colony Optimization. International Journal of Innovative Computing, Information and Control 5(12), 5081–5092 (2009)
Amer, D., Bouaziz, A.: An artificial bee colony algorithm for image contrast enhancement. Swarm and Evolutionary Computation 16(6), 69–84 (2014)
Afandi, A.N., Miyauchi, H.: Improved artificial bee colony algorithm considering harvest season for computing economic dispatch on power system. IEEE Transactions on Electrical and Electronic Engineering 9(3), 251–257 (2014)
Ghani, A.A., Mohamad-Saleh, J.: Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation. Turkish Journal of Electrical Engineering and Computer Sciences 22(3), 620–626 (2014)
Onder, B., Tasgetiren, M.F.: An artificial bee colony algorithm for the economic lot scheduling problem. International Journal of Production Research 42(4), 1150–1170 (2014)
Xiangtao, L., Minghao, Y.: Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Computing and Applications 24(3), 723–734 (2014)
Tizhoosh, H.R.: Opposition-based Learning: a new scheme for machine intelligence. In: Proceedings of on Computational Intelligence for Modeling,Control and Automation, pp. 695–701 (2005)
Tizhoosh, H.R.: Reinforcement Learning Based on Actions and Opposite Actions. In: Proceedings of on Artificial Intelligence and Machine Learning, pp. 19–21 (2005)
Qing-zheng, X., Lei, W., Bao-min, H., et al.: Opposition-Based Differential Evolution Using the Current Optimum for Function Optimization. Journal of Applied Sciences 29(3), 308–315 (2011)
Haiping, M., Xieyong, R., Baogen, J.: Oppositional ant colony optimization algorithm and its application to fault monitoring. In: Proceedings of Chinese Control Conference, pp. 3895–3898 (2010)
Juan, L., Yi-wen, Z., Sen-lin, M.: Improved opposition-based shuffled frog leaping algorithm for function optimization problems. Application Research of Computers 30(3), 760–763 (2013)
Shahryar, R., Hamid, R.T., Magdy, M.A.S.: Opposition versus randomness in soft computing techniques. Applied Soft Computing 8(2), 906–918 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhao, J., Lv, L., Sun, H. (2015). Artificial Bee Colony Using Opposition-Based Learning. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-12286-1_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12285-4
Online ISBN: 978-3-319-12286-1
eBook Packages: EngineeringEngineering (R0)