Abstract
Differential Evolution (DE) is a very popular optimization algorithm for solving numerical optimization problems. It is simple yet powerful algorithm, which has shown effective performance in many optimization problems. In this paper, DECSO that uses the Abandon operator of Cuckoo search to improve the exploration ability of the original DE was proposed. The experimental studies on ten well-known benchmark functions have shown that the proposed approach has efficient search power and fast convergence.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Gu, J., Gu, G.: Differential Evolution with a local search operator. In: International Asia Conference on Informatics in Control, Automation and Robotics, vol. 2, pp. 480–483 (2010)
Wang, Y., Cai, Z., Zhang, Q.: Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters. IEEE Transactions on Evolutionary Computation 15, 55–66 (2011)
Chen, C.: Differential evolution based on a novel double-population strategy. In: 2nd International Conference in Signal Processing System, vol. 3, pp. 649–652 (2010)
Huang, V.L., Qin, A.K., Suganthan, P.N.: Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization. In: IEEE Congress on Evolutionary Computation pp.17–24 (2006)
Liu, Z., Zhang, Y., Ning, Z.: Differential Evolution based on Improved Mutation Strategy. In: 2nd International Conference on Computer Engineering and Technology, vol. 4, pp. 228–231 (2010)
Miao, X., Fan, P., Wang, J., Li, C.: Differential Evolution Based on Adaptive Mutation. In: 2nd International Asia Conference on Informatics in Control, Automation and Robotics, vol. 3, pp. 113–116 (2010)
Yang, X.S., Deb, S.: Cuckoo search via Lévy Flights. In: World Congress on Nature & Biologically Inspired Computing, pp. 210–214. IEEE Publications (2009)
Yang, X.S., Deb, S.: Engineering Optimisation by Cuckoo Search. Int. J. Mathematical Modelling and Numerical Optimisation 1, 330–343 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Musigawan, P., Chiewchanwattana, S., Sunat, K. (2012). Improved Differential Evolution via Cuckoo Search Operator. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_56
Download citation
DOI: https://doi.org/10.1007/978-3-642-34475-6_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34474-9
Online ISBN: 978-3-642-34475-6
eBook Packages: Computer ScienceComputer Science (R0)