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Neighborhood Combination Strategies for Solving the Bi-objective Max-Bisection Problem

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13393))

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Abstract

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

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Notes

  1. 1.

    More information about the benchmark instances of max-cut problem can be found on this website: https://www.stanford.edu/$/sim$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. Discret. 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-Verlag New York, Inc., Secaucus, NJ, USA (2007). https://doi.org/10.1007/978-0-387-36797-2

  6. Kochenberger, G.A., Glover, F., Hao, J.-K., Lü, Z., Wang, H., Glover, F.: Solving large scale max cut problems via tabu search. J. Heuristics 19, 565–571 (2013)

    Article  Google Scholar 

  7. 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 

  8. 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 

  9. Wu, Q., Hao, J.-K.: Memetic search for the max-bisection problem. Comput. Oper. Res. 40, 166–179 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  10. 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 

  11. Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_84

    Chapter  Google Scholar 

  12. 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 

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Acknowledgments

The work in this paper was supported by the Fundamental Research Funds for the Central Universities (Grant No. A0920502051728–53) and supported by the West Light Foundation of Chinese Academy of Science (Grant No: Y4C0011006).

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Authors and Affiliations

  1. Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, People’s Republic of China

    Rong-Qiang Zeng

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

    Rong-Qiang Zeng

  3. LERIA, Universitéd’Angers, 2, Boulevard Lavoisier, 49045, Angers Cedex 01, France

    Matthieu Basseur

Authors
  1. Rong-Qiang Zeng
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  2. Matthieu Basseur
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Corresponding author

Correspondence to Rong-Qiang Zeng .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi'an Polytechnic University, Xi'an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Zeng, RQ., Basseur, M. (2022). Neighborhood Combination Strategies for Solving the Bi-objective Max-Bisection Problem. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_10

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  • DOI: https://doi.org/10.1007/978-3-031-13870-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13869-0

  • Online ISBN: 978-3-031-13870-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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