Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Science and Big Data Engineering

IScIDE 2017: Intelligence Science and Big Data Engineering pp 101–112Cite as

A Hybrid Multi-swarm PSO Algorithm Based on Shuffled Frog Leaping Algorithm

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

Abstract

As an effective swarm intelligence algorithm, multi-swarm particle swarm optimization (PSO) has better search ability than single-swarm PSO. In order to enhance the ability of group communication as well as improve the ability of local search, this paper proposes a hybrid multi-swarm PSO algorithm. Three strategies have been proposed, which are multi-swarm strategy, update strategy and cooperation strategy. A new way of grouping the particle swarms is put forward by calculating the fitness value of particles. In each group, the particles updates according to the formula which is morphed from the shuffled frog leaping algorithm. Moreover, a new information communication strategy is proposed. The cooperation of these three strategies maintains the diversity of algorithm and improves the ability of searching the optimal solution. Finally, the experimental results on the benchmark functions verify the effectiveness of the proposed PSO.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)

    Google Scholar 

  2. Xie, X.F., Zhang, W.J., Yang, Z.L.: Overview of particle swarm optimization. J. Kongzhi Yu Juece/Control Decis. 18(2), 129–134 (2003). Beijing

    Google Scholar 

  3. Tang, K., Li, Z., Luo, L., et al.: Multi-strategy adaptive particle swarm optimization for numerical optimization. J. Eng. Appl. Artif. Intell. 37, 9–19 (2015)

    Article  Google Scholar 

  4. Oca, M.A.M.D., Aydın, D., Stützle, T.: An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re) design of optimization algorithms. J. Soft Comput. 15(11), 2233–2255 (2011)

    Article  Google Scholar 

  5. García-Nieto, J., Alba, E.: Restart particle swarm optimization with velocity modulation: a scalability test. J. Soft Comput. 15(11), 2221–2232 (2011)

    Article  Google Scholar 

  6. Gülcü, S., Kodaz, H.: A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization. J. Eng. Appl. Artif. Intell. 45, 33–45 (2015)

    Article  Google Scholar 

  7. Xu, X., Tang, Y., Li, J., et al.: Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy. J. Appl. Soft Comput. 29, 169–183 (2015)

    Article  Google Scholar 

  8. Li, J., Sun, H., Shi, X., et al.: The research on multi-swarm particle swarm optimization algorithm and hybrid frog leaping algorithm. J. J. Chin. Mini-Micro Comput. Syst. 34(9), 2164–2168 (2013)

    Google Scholar 

  9. Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using shuffled frog leaping algorithm. J. Water Resour. Plan. Manag. 129(3), 210–225 (2003)

    Article  Google Scholar 

  10. Zhang, X., Du, Y., Qin, Z., Qin, G., Lu, J.: A modified particle swarm optimizer for tracking dynamic systems. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 592–601. Springer, Heidelberg (2005). doi:10.1007/11539902_72

    Chapter  Google Scholar 

  11. Luo, D.X., Zhou, Y.Q., Huang, H.J., et al.: Multi-colony particle swarm optimization algorithm. Comput. Eng. Appl. 46(19), 51–54 (2010)

    Google Scholar 

  12. Muzaffar, E., Kevin, L., Fayzul, P.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. J. Eng. Optim. 38(2), 129–154 (2006)

    Article  MathSciNet  Google Scholar 

  13. Rahimi-Vahed, A., Mirzaei, A.H.: A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. J. Comput. Ind. Eng. 53(4), 642–666 (2007)

    Article  Google Scholar 

  14. Talbi, E.G.: Metaheuristics: from design to implementation. J. Proc. SPIE – Int. Soc. Opt. Eng. 42(4), 497–541 (2009)

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61271385 and 61572241), the Foundation of the Peak of Six Talents of Jiangsu Province (No. 2015-DZXX-024), and the Fifth “333 High Level Talented Person Cultivating Project” of Jiangsu Province.

Author information

Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Hongfei Bao & Fei Han

Authors
  1. Hongfei Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongfei Bao .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bao, H., Han, F. (2017). A Hybrid Multi-swarm PSO Algorithm Based on Shuffled Frog Leaping Algorithm. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67777-4_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation