Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Computing

ICIC 2011: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence pp 203–210Cite as

A New Multi-swarm Multi-objective Particle Swarm Optimization Based on Pareto Front Set

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6839))

Abstract

In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Press, New York (1995)

    Chapter  Google Scholar 

  2. Ho, S.L., Yang, S., Ni, G., Lo, E.W.C., Wong, H.C.: A Particle Swarm Optimization-based Method for Multiobjective Design Optimizations. IEEE Trans. on Magn. 41, 1756–1759 (2005)

    Article  Google Scholar 

  3. Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Trans. on Evolu. Comp. 8, 240–255 (2004)

    Article  Google Scholar 

  4. Tan, K.C., Khor, E.F., Lee, T.H.: Evolutionary Multi-objective Optimization: Algorithms and Applications. Springer, New York (2005)

    MATH  Google Scholar 

  5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolu. Comp. 6, 182–197 (2002)

    Article  Google Scholar 

  6. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computation Engineering Networks Lab (TIK), Swiss Fed. Inst. Technol (ETH), Zurich, Switzerland, Tech. Rep. 103 (2001)

    Google Scholar 

  7. Coello, C., Pulido, C.A., Lechuga, M.S.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Trans. on Evolu. Comp. 8, 256–279 (2004)

    Article  Google Scholar 

  8. Yen, G.G., Daneshyari, M.: Diversity-based Information Exchange Among Multiple Swarms in Particle Swarm Optimization. Int. J. Compu. Intel. Appl. 7, 57–75 (2008)

    Article  MATH  Google Scholar 

  9. Leong, W.F., Yen, G.G.: PSO-based Multi-objective Optimization with Dynamic Population Size and Adaptive Local Archives. IEEE Tran. Syst., Man, Cyb. B, Cyb. 38, 1270–1293 (2008)

    Article  Google Scholar 

  10. Cooren, Y., Clerc, M., Siarry, P.: MO-TRIBES, Adaptive Multiobjective Particle Swarm Optimization Algorithm, Compu. Opt. and Appl. 30(2), 60–80 (2010)

    MATH  Google Scholar 

  11. Cooren, Y., Clerc, M., Siarry, P.: Performance Evaluation of TRIBES, Adaptive Particle Swarm optimization algorithm. Swarm Intel. 3, 149–178 (2009)

    Article  MATH  Google Scholar 

  12. Khor, E.F., Tan, K.C., Lee, T.H., Goh, C.K.: A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization. Artificial Intel. Rev. 23, 31–56 (2005)

    Article  Google Scholar 

  13. Fonseca, C.M., Fleming, P.J.: Genetic Algorithm for Multi-objective Optimization, Formulation, Discussion and Generalization. In: Genetic Algorithms: Proceedings of the Fifth International Conference, pp. 416–423 (1993)

    Google Scholar 

  14. Khor, E.F., Tan, K.C., Lee, T.H., Goh, C.K.: A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization. Artificial Intelligence Review 23, 31–56 (2005)

    Article  Google Scholar 

  15. Jeong, S., Hasegawa, S., Shimoyama, K., Obayashi, S.: Development and Investigation of Efficient GA/PSO-Hybrid Algorithm Applicable to Real-World Design Optimization. IEEE Compu. Intel. Mag. 30(2), 36–44 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria, 0001, South Africa

    Yanxia Sun & Barend Jacobus van Wyk

  2. School of Engineering, University of South Africa, Pretoria, 0003, South Africa

    Zenghui Wang

Authors
  1. Yanxia Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barend Jacobus van Wyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zenghui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  4. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    M. Michael Gromiha

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sun, Y., van Wyk, B.J., Wang, Z. (2012). A New Multi-swarm Multi-objective Particle Swarm Optimization Based on Pareto Front Set. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25944-9_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25943-2

  • Online ISBN: 978-3-642-25944-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation