Abstract
The cuckoo search algorithm is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, we use a new search strategy based on orthogonal learning strategy to enhance the exploitation ability of the basic cuckoo search algorithm. In order to verify the performance of our approach, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained.
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Acknowledgments
This research is fully supported by Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University under Grant No. ZSDZZZZXK37 and the Fundamental Research Funds for the Central Universities Nos. 11CXPY010.
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Li, X., Wang, J. & Yin, M. Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput & Applic 24, 1233–1247 (2014). https://doi.org/10.1007/s00521-013-1354-6
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DOI: https://doi.org/10.1007/s00521-013-1354-6