Abstract
The multi-level approach is known to be a highly effective metaheuristic framework for tackling several types of combinatorial optimization problems, which is one of the best performing approaches for the graph partitioning problems. In this paper, we integrate the multi-level approach into the hypervolume-based multi-objective local search algorithm, in order to solve the bi-criteria max-cut problem. The experimental results indicate that the proposed algorithm is very competitive, and the performance analysis sheds lights on the ways to further improvements.
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Notes
- 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., Xu, HY., Hu, ZY., Wen, Y. (2017). Hypervolume-Based Multi-level Algorithm for the Bi-criteria Max-Cut Problem. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_35
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