Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2017: Intelligent Data Engineering and Automated Learning – IDEAL 2017 pp 508–515Cite as

Solving the Bi-criteria Max-Cut Problem with Different Neighborhood Combination Strategies

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Local search is known to be a highly effective metaheuristic framework for solving a number of classical combinatorial optimization problems, which strongly depends on the characteristics of neighborhood structure. In this paper, we integrate different neighborhood combination strategies into the hypervolume-based multi-objective local search algorithm, in order to deal with the bi-criteria max-cut problem. The experimental results indicate that certain combinations are superior to others and the performance analysis sheds lights on the ways to further improvements.

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

Notes

  1. 1.

    More information about the benchmark instances of max-cut problem can be found on this website: http://www.stanford.edu/~yyye/yyye/Gset/.

  2. 2.

    More information about the performance assessment package can be found on this website: http://www.tik.ee.ethz.ch/pisa/assessment.html.

References

  1. Angel, E., Gourves, E.: Approximation algorithms for the bi-criteria weighted max-cut problem. Discrete Appl. Math. 154, 1685–1692 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  2. Basseur, M., Liefooghe, A., Le, K., Burke, E.: The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems. J. Heuristics 18(2), 263–296 (2012)

    Article  Google Scholar 

  3. Basseur, M., Zeng, R.-Q., Hao, J.-K.: Hypervolume-based multi-objective local search. Neural Comput. Appl. 21(8), 1917–1929 (2012)

    Article  Google Scholar 

  4. Benlic, U., Hao, J.-K.: Breakout local search for the max-cut problem. Eng. Appl. Artif. Intell. 26, 1162–1173 (2013)

    Article  MATH  Google Scholar 

  5. Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer, Secaucus (2007)

    MATH  Google Scholar 

  6. Marti, R., Duarte, A., Laguna, M.: Advanced scatter search for the max-cut problem. INFORMS J. Comput. 21(1), 26–38 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Shylo, V.P., Shylo, O.V.: Solving the maxcut problem by the global equilibrium search. Cybern. Syst. Anal. 46(5), 744–754 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Wu, Q., Wang, Y., Lü, Z.: A tabu search based hybrid evolutionary algorithm for the max-cut problem. Appl. Soft Comput. 34, 827–837 (2015)

    Article  Google Scholar 

  9. Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30217-9_84

    Chapter  Google Scholar 

  10. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. Evol. Comput. 3, 257–271 (1999)

    Article  Google Scholar 

Download references

Acknowledgments

The work in this paper was supported by the Fundamental Research Funds for the Central Universities (Grant No. A0920502051722-53) and supported by the West Light Foundation of Chinese Academy of Science (Grant No: Y4C0011001).

Author information

Authors and Affiliations

  1. EHF Key Laboratory of Science, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, People’s Republic of China

    Li-Yuan Xue

  2. School of Mathematics, Southwest Jiaotong University, Chengdu, 610031, Sichuan, People’s Republic of China

    Rong-Qiang Zeng

  3. Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu, 610041, Sichuan, People’s Republic of China

    Rong-Qiang Zeng, Zheng-Yin Hu & Yi Wen

Authors
  1. Li-Yuan Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rong-Qiang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zheng-Yin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rong-Qiang Zeng .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xue, LY., Zeng, RQ., Hu, ZY., Wen, Y. (2017). Solving the Bi-criteria Max-Cut Problem with Different Neighborhood Combination Strategies. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68935-7_55

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation