Skip to main content
SpringerLink
Log in

An Adaptive Particle Swarm Optimization Using Hybrid Strategy

  • Conference paper
  • First Online:
Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 874))

Included in the following conference series:

  • 680 Accesses

Abstract

As an intelligent algorithm inspired by the foraging behavior in nature, particle swarm optimization (PSO) is famous for its few parameters, easy to implement and higher convergence accuracy. However, PSO also has a weakness over the local search, also called the prematurity, which resulted in the convergence accuracy reduced and the convergence speed slowed. For this, extremal optimization (EO), an excellent local search algorithm, has been introduced to be improved (CEO) and enhance the local search of PSO. Meanwhile, for improving its global search further, an improved opposition-based learning based on refraction principle (UOBL) has been chosen to enhance the global search of PSO, which is a better global optimization algorithm. In order to balance both of PSO to improve its optimization performance further, an adaptive hybrid PSO based on UOBL and CEO (AHOPSO-CEO) is proposed in this article. The large number of experiment results and analysis reveals that AHOPSO-CEO achieves better performance with other algorithms on the convergence speed and convergence accuracy for optimization problems.

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 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

Institutional subscriptions

References

  1. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1942–1948. IEEE Press (1995)

    Google Scholar 

  2. Wang, L., Yang, B., Chen, Y.: Improving particle swarm optimization using multi-layer searching strategy. Inf. Sci. 274(8), 70–94 (2014)

    Article  Google Scholar 

  3. Tran, D.C., Wu, Z., Wang, H.: A new approach of diversity enhanced particle swarm optimization with neighborhood search and adaptive mutation. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8835, pp. 143–150. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12640-1_18

    Chapter  Google Scholar 

  4. Zuo, X.Q., Xiao, L.: A DE and PSO based hybrid algorithm for dynamic optimization problems. Soft Comput. 18(7), 1405–1424 (2014)

    Article  Google Scholar 

  5. Elsayed, S.M., Sarker, R.A., Mezura-Montes, E.: Self-adaptive mix of particle swarm methodologies for constrained optimization. Inf. Sci. 277, 216–233 (2014)

    Article  MathSciNet  Google Scholar 

  6. Cheng, R., Jin, Y.C.: A social learning particle swarm optimization algorithm for scalable optimization. Inf. Sci. 291, 43–60 (2015)

    Article  MathSciNet  Google Scholar 

  7. Schmitt, M., Wanka, R.: Particle swarm optimization almost surely finds local optima. Theor. Comput. Sci. 561, 57–72 (2015)

    Article  MathSciNet  Google Scholar 

  8. Boettcher, S., Percus, A.G.: Optimization with extremal dynamics. Phys. Rev. Lett. 86, 5211–5214 (2001)

    Article  Google Scholar 

  9. Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Phys. Rev. Lett. 71(24), 4083–4086 (1993)

    Article  Google Scholar 

  10. Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: an explanation of the 1/f noise. Phys. Rev. Lett. 59(59), 381–384 (1987)

    Article  Google Scholar 

  11. Boettcher, S., Percus, A.G.: Extremal optimization at the phase transition of the three-coloring problem. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(6Pt2), 66703 (2004)

    Article  Google Scholar 

  12. Chen, Y.W., Lu, Y.Z., Yang, G.K.: Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling. Int. J. Adv. Manuf. Technol. 36(9), 959–968 (2008)

    Article  Google Scholar 

  13. Chen, Y.W., Lu, Y.Z., Chen, P.: Optimization with extremal dynamics for the traveling salesman problem. Phys. A Stat. Mech. Appl. 385(1), 115–123 (2007)

    Article  Google Scholar 

  14. Chen, M.R., Lu, Y.Z., Yang, G.K.: Multi-objective extremal optimization with applications to engineering design. J. Zhejiang Univ. - Sci. A: Appl. Phys. Eng. 8(12), 1905–1911 (2007)

    Article  Google Scholar 

  15. Paczuski, M., Maslov, S., Bak, P.: Avalanche dynamics in evolution, growth, and depinning models. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdisc. Top. 53(1), 414–443 (1996)

    Google Scholar 

  16. Azadehgan, V., Jafarian, N., Jafarieh, F.: A new hybrid algorithm for optimization based on artificial bee colony and extremal optimization. In: IEEE Conference Anthology, pp. 1–6. IEEE (2014)

    Google Scholar 

  17. Chen, M.R., Zeng, G.Q., Zeng, W., et al.: A novel artificial bee colony algorithm with integration of extremal optimization for numerical optimization problems. In: Evolutionary Computation, pp. 242–249. IEEE (2014)

    Google Scholar 

  18. Li, X., Luo, J., Chen, M.R., et al.: An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation. Inf. Sci. Int. J. 192(6), 143–151 (2012)

    Google Scholar 

  19. Ghandehari, N., Miranian, E., Maddahi, M.: Hybrid extremal optimization and glowworm swarm optimization. In: Das, V. (ed.) Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. LNEE, vol. 150, pp. 83–89. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-3363-7_10

    Chapter  Google Scholar 

  20. Chen, M.R., Li, X., Zhang, X., et al.: A novel particle swarm optimizer hybridized with extremal optimization. Appl. Soft Comput. 10(2), 367–373 (2010)

    Article  Google Scholar 

  21. Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of International Conference on Intelligent Agent, Web Technologies and Internet Commerce, pp. 695–701. IEEE Press, Vienna (2005)

    Google Scholar 

  22. Wang, H., Li, H., Liu, Y., et al.: Opposition-based particle swarm algorithm with cauchy mutation. In: IEEE Congress on Evolutionary Computation, pp. 4750–4756. IEEE Press, Singapore (2007)

    Google Scholar 

  23. Wang, H., Zhijian, W., Rahnamayan, S., et al.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. 181(20), 4699–4714 (2011)

    Article  MathSciNet  Google Scholar 

  24. Shao, P., Wu, Z., Zhou, X., et al.: Improved particle swarm optimization algorithm based on opposition learning of refraction. Acta Electronica Sin. 43(11), 2137–2144 (2015)

    Google Scholar 

  25. Zeng, J.C., Cui, Z.H.: A guaranteed global convergence particle swarm optimizer. J. Comput. Res. Dev. 3066(8), 762–767 (2004)

    MathSciNet  MATH  Google Scholar 

  26. Lu, R.F., Wang, X.Y.: Convergence analysis of particle swarm optimization algorithm. Sci. Technol. Eng. 4(14), 25–32 (2008)

    Google Scholar 

  27. Shao, P., Wu, Z., Zhou, X., et al.: FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput. 21(10), 2631–2642 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 70971043 and 61763019), the Science and Technology plan project of Jiangxi Province (No. GJJ160409, GJJ161076).

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi, China

    Peng Shao, Yinglong Wang & Guangquan Li

  2. Computer School, Wuhan University, Wuhan, 430072, Hubei, China

    Zhijian Wu

  3. School of Information Science and Technology, Jiujiang University, Jiujiang, 332005, Jiangxi, China

    Hu Peng

Authors
  1. Peng Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhijian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hu Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yinglong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guangquan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Shao .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shao, P., Wu, Z., Peng, H., Wang, Y., Li, G. (2018). An Adaptive Particle Swarm Optimization Using Hybrid Strategy. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1651-7_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1650-0

  • Online ISBN: 978-981-13-1651-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation