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Cuckoo Search Algorithm with Chaotic Maps

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  • Lijin Wang
  • Yiwen Zhong

Abstract

Cuckoo search algorithm is a novel nature-inspired optimization technique based on the obligate brood parasitic behavior of some cuckoo species. It iteratively employs Lévy flights random walk with a scaling factor and biased/selective random walk with a fraction probability. Unfortunately, these two parameters are used in constant value schema, resulting in a problem sensitive to solution quality and convergence speed. In this paper, we proposed a variable value schema cuckoo search algorithm with chaotic maps, called CCS. In CCS, chaotic maps are utilized to, respectively, define the scaling factor and the fraction probability to enhance the solution quality and convergence speed. Extensive experiments with different chaotic maps demonstrate the improvement in efficiency and effectiveness.

Suggested Citation

  • Lijin Wang & Yiwen Zhong, 2015. "Cuckoo Search Algorithm with Chaotic Maps," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, July.
    • Handle: RePEc:hin:jnlmpe:715635
    DOI: 10.1155/2015/715635
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    Cited by:

    1. Nguyen, Thang Trung & Vo, Dieu Ngoc & Dinh, Bach Hoang, 2018. "An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems," Energy, Elsevier, vol. 155(C), pages 930-956.
    2. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.

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