IDEAL 2017: Intelligent Data Engineering and Automated Learning – IDEAL 2017 pp 508-515 | Cite as
Solving the Bi-criteria Max-Cut Problem with Different Neighborhood Combination Strategies
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.
Keywords
Multi-objective optimization Hypervolume contribution Neighborhood combination Local search Max-cut problemNotes
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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