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International Conference on Neural Computing for Advanced Applications

NCAA 2021: Neural Computing for Advanced Applications pp 403–416Cite as

An Improved Cloud Particles Optimizer for Function Optimization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

Abstract

Evolutionary algorithm is a popular and effective method in optimization field. As one of the optimization algorithms, cloud particles optimizer (CPEA) has shown to be effective in solving problems with different characteristics. However, due to the powerful local search ability, premature convergence is a significant disadvantage of CPEA optimizer. To alleviate this problem, an improved cloud particles optimizer named ICPEA is proposed. In ICPEA, the fluid operation is designed to explore the evolution direction, while the solid operation is employed to improve the exploitation efficiency. Moreover, the dynamical selection strategies of control parameters are employed to cope with premature convergence issues. In order to demonstrate the effectiveness of ICPEA, CEC2014 test suites are used for simulating. The experimental results affirm that ICPEA is a competitive optimizer compared to CPEA algorithm and several state-of-the-art optimizers.

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Acknowledgments

This research is partly supported by the Doctoral Foundation of Xi’an University of Technology under Grant 112–451116017, the National Natural Science Foundation of China under Project Code under Grant 61803301, and the National Natural Science Foundation of China under Project Code under Grant 61773314.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Wei Li, Haonan Luo, Junqing Yuan & Zhou Lei

  2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an, 710048, China

    Wei Li & Lei Wang

Authors
  1. Wei Li
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  2. Haonan Luo
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  3. Junqing Yuan
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  4. Zhou Lei
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  5. Lei Wang
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Corresponding author

Correspondence to Wei Li .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Cite this paper

Li, W., Luo, H., Yuan, J., Lei, Z., Wang, L. (2021). An Improved Cloud Particles Optimizer for Function Optimization. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_29

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5187-8

  • Online ISBN: 978-981-16-5188-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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