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Chaotic cuckoo search

Abstract

This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS method, 12 chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-seven benchmark functions and an engineering case are utilized to investigate the efficiency of CCS. The results clearly demonstrate that the performance of CCS together with a suitable chaotic map is comparable as well as superior to that of the CS and other metaheuristic algorithms.

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Acknowledgments

This work was supported by Research Fund for the Doctoral Program of Jiangsu Normal University (No. 9213614102) and National Natural Science Foundation of China (No. 61305149).

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Correspondence to Gai-Ge Wang.

Additional information

S. Deb is pioneer of cuckoo search algorithm.

Communicated by S. Deb, T. Hanne and S. Fong.

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Cite this article

Wang, GG., Deb, S., Gandomi, A.H. et al. Chaotic cuckoo search. Soft Comput 20, 3349–3362 (2016). https://doi.org/10.1007/s00500-015-1726-1

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Keywords

  • Global optimization
  • Cuckoo search
  • Chaotic maps
  • Multimodal function