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Cuckoo Search Algorithm Based on Individual Knowledge Learning

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 952))

Abstract

Cuckoo search (CS) is a one of the most efficient evolutionary for global optimization, and widely applied to solve diverse real-world problems. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploitation and exploration. To cope with these issues, a new CS extension based on individual knowledge learning (IKL-CS) is proposed. In this study, knowledge learning based on individual history is introduced into the CS algorithm. Individuals are constantly adjusted and optimized to use their historical knowledge in the optimization process, and communicate with each other to use their own knowledge. The accuracy and performance of the proposed approach are evaluated by eighteen classic benchmark functions. Statistical comparisons of our experimental results showed that the proposed IKL-CS algorithm made an appropriate trade-off between exploration and exploitation. Comparing the proposed I-PKL-CS with various evolutionary CS algorithms, the results demonstrated that IKL-CS is a competitive new type of algorithm.

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Acknowledgements

This work was supported by the scientific research project of Hubei Provincial Department of Education (No. B2017314), National Natural Science Foundation of China (No. 61672391), Innovation team of the Provincial Education Department (No. T201631), and Hubei provincial teaching research project (No. 2016446).

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Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan, 430072, China

    Juan Li & Yuan-Xiang Li

  2. School of Information Engineering, Wuhan Technology and Business University, Wuhan, 430065, China

    Juan Li & Jie Zou

Authors
  1. Juan Li
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  2. Yuan-Xiang Li
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  3. Jie Zou
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Corresponding author

Correspondence to Yuan-Xiang Li .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Cite this paper

Li, J., Li, YX., Zou, J. (2018). Cuckoo Search Algorithm Based on Individual Knowledge Learning. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_41

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  • DOI: https://doi.org/10.1007/978-981-13-2829-9_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2828-2

  • Online ISBN: 978-981-13-2829-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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