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Comparative Study of Chaos-Embedded Particle Swarm Optimization

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Intelligent Information Processing XI (IIP 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 643))

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Abstract

Particle swarm optimization (PSO) is a population-based stochastic search algorithm that has been widely used to solve many real-world problems. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima when addressing complex multimodal problems. In this paper, we propose a chaos-embedded particle swarm optimization algorithm (CEPSO). In CEPSO, the chaos-based swarm initialization is first applied to yield high-quality initial particles with better stability. Afterwards the chaotic inertia weight and the chaotic sequence based random numbers are introduced into the velocity update scheme for PSO to improve its global and local search capabilities. In addition, two different mutation strategies (chaos and levy) are utilized to enhance the swarm diversity without being trapped in local optima. Finally, the CEPSO proposed in this work is compared with several classical PSOs on a set of well-known benchmark functions. Experimental results show that CEPSO can achieve better performance compared to several other PSO variants in terms of the solution accuracy and convergence rate.

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Acknowledgment

This work is fully supported by the Program of the Science and Technology Department of Xinjiang Uygur Autonomous Region (No. 2022D01A16) and the Program of the Applied Technology Research and Development of Kashi Prefecture (No. KS2021026).

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Authors and Affiliations

  1. School of Computer Science and Technology, Kashi University, Xuefu Avenue, Kashi City, 844006, Xinjiang, China

    Dongping Tian, Bingchun Li, Chen Liu, Haiyan Li & Ling Yuan

Authors
  1. Dongping Tian
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  2. Bingchun Li
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  3. Chen Liu
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  4. Haiyan Li
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  5. Ling Yuan
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Corresponding author

Correspondence to Dongping Tian .

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Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. IRD, Sorbonne University, Bondy, France

    Jean-Daniel Zucker

  3. Nanyang Technological University, Singapore, Singapore

    Bo An

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Tian, D., Li, B., Liu, C., Li, H., Yuan, L. (2022). Comparative Study of Chaos-Embedded Particle Swarm Optimization. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-03948-5_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-03947-8

  • Online ISBN: 978-3-031-03948-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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