Skip to main content
SpringerLink
Log in

Research on Improvement of Particle Swarm Optimization

  • Conference paper
  • First Online:
Cyber Security Intelligence and Analytics (CSIA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 928))

Abstract

Although the particle swarm optimization algorithm has simple principle, few parameters and easy implementation, the particle swarm optimization algorithm is easy to fall into local optimum on multi-mode function and the local search ability is relatively weak. In this paper, the improvement of these two defects is carried out. The particle motion formula with learning model is added, and the generation strategy of a guided vector is added to improve the particle swarm optimization algorithm. The improved algorithm has a two-layer structure, and finally the research direction is prospected.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhang Q, Liu W, Meng X, et al (2017) Vector coevolving particle swarm optimization algorithm. Inf Sci 394(C):273–298

    Google Scholar 

  2. Li R, Shen Y, Liu J (2015) Improved adaptive particle swarm optimization algorithm. Comput Eng Appl 51(13):31–36. (in Chinese)

    Google Scholar 

  3. Wang H, Sun H, Li C et al (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci Int J 223(2):119–135

    MathSciNet  Google Scholar 

  4. Fei Yu, Yuanxiang L, Bo W et al (2014) Application of lens imaging inverse learning strategy in particle swarm optimization algorithm. Chin J Electron 42(2):230–235

    Google Scholar 

  5. Zhou J, Fang W, Wu X, et al (2016) An opposition-based learning competitive particle swarm optimizer. In: Evolutionary computation. IEEE

    Google Scholar 

  6. Wang Y, Li B, Weise T et al (2010) Self-adaptive learning based particle swarm optimization. Inf Sci 81(20):4515–4538

    Article  MathSciNet  Google Scholar 

  7. Zhang WJ, Xie XF (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: 2003 IEEE international conference on systems, man and cybernetics, vol 4. IEEE, pp 3816–3821

    Google Scholar 

  8. Moore PW, Venayagamoorthy GK (2006) Evolving digital circuits using hybrid particle swarm optimization and differential evolution. Int J Neural Syst 16(03):163–177

    Google Scholar 

  9. Epitropakis MG, Plagianakos VP, Vrahatis MN (2012) Evolving cognitive and social experience in particle swarm optimization through differential evolution: a hybrid approach. Inf Sci 216:50–92

    Article  Google Scholar 

  10. Zhang M, Zhang W, Sun Y (2009) Chaotic co-evolutionary algorithm based on differential evolution and particle swarm optimization. In: 2009 IEEE international conference on automation and logistics, ICAL 2009. IEEE, pp 885–889

    Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Project No. 51678375), Natural Science Foundation of Liaoning Province (Project No. 2015020603), and the basic scientific research project of Liaoning Higher Education (Project No. LJZ2017009).

Author information

Authors and Affiliations

  1. School of Management, Shenyang Jianzhu University, Shenyang, 110168, China

    Chunguang Chang & Xi Wu

Authors
  1. Chunguang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xi Wu .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Zheng Xu

  2. University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

  4. Kennesaw State University, Marietta, GA, USA

    Reza Parizi

  5. Manchester Metropolitan University, Stockport, UK

    Mohammad Hammoudeh

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chang, C., Wu, X. (2020). Research on Improvement of Particle Swarm Optimization. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_174

Download citation

Publish with us

Policies and ethics

Search

Navigation