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An Improved Squirrel Search Algorithm with Reproduction and Competition Mechanisms

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

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Abstract

The performance of the recently-proposed Squirrel Search Algorithm (SSA) is improved in this paper. SSA is a swarm intelligence algorithm that simulates the dynamic foraging behavior of squirrels. The traditional SSA is prone to premature convergence when solving optimization problems. This work proposed a propagation and diffusion search mechanism to alleviate these drawbacks by expand the search space using the Invasive Weed Algorithm (IWO). The proposed algorithm, which called SSIWO, has high ability to improve the exploration and local optimal avoidance of SSA. In order to investigate the performance proposed SSIWO algorithm, several experiments are conducted on eight benchmark functions and using three algorithms. The experimental results show the superior performance of the proposed SSIWO algorithm to determine the optimal solutions of the benchmark function problems.

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Acknowledgments

The work for this paper was supported by the National Natural Science Foundation of China (Grant nos. 61572446, 61602424, and U1804262), Key Scientific and Technological Project of Henan Province (Grant nos. 174100510009, 192102210134), and Key Scientific Research Projects of Henan High Educational Institution (18A510020).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Xuncai Zhang & Kai Zhao

Authors
  1. Xuncai Zhang
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  2. Kai Zhao
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Corresponding author

Correspondence to Xuncai Zhang .

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Editors and Affiliations

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

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Zhang, X., Zhao, K. (2020). An Improved Squirrel Search Algorithm with Reproduction and Competition Mechanisms. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_29

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

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