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Self-adaptive Percolation Behavior Water Cycle Algorithm

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Intelligent Computing Theories and Methodologies (ICIC 2015)

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Abstract

Water cycle algorithm is a new meta-heuristic optimization algorithm based on the observation of water cycle and how rivers and streams flow downhill towards the sea in the real world. In this paper, a new self-adaptive water cycle algorithm with percolation behavior is proposed. The percolation behavior is introduced to accelerate the convergence speed of proposed algorithm. At the same time, a self-adaptive rainfall process can generate the new stream, more and more new position can be explored, consequently, increasing the diversity of population. Eight typical benchmark functions are tested, the simulation results show that the proposed algorithm is feasible and effective than basic water cycle algorithm, and demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space.

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Acknowledgments

This work is supported by National Science Foundation of China under Grants No.61165015; 61463007.

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Shilei Qiao, Yongquan Zhou, Rui Wang & Yuxiang Zhou

  2. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning, 530006, China

    Yongquan Zhou

Authors
  1. Shilei Qiao
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  2. Yongquan Zhou
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  3. Rui Wang
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  4. Yuxiang Zhou
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Corresponding author

Correspondence to Yongquan Zhou .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2015 Springer International Publishing Switzerland

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Qiao, S., Zhou, Y., Wang, R., Zhou, Y. (2015). Self-adaptive Percolation Behavior Water Cycle Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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