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.
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- 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.
More information about the performance assessment package can be found on this website: http://www.tik.ee.ethz.ch/pisa/assessment.html.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-319-68935-7_55
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