NukCP: An Improved Local Search Algorithm for Maximum k-Club Problem

The maximum k-club problem (MkCP) is an important clique relaxation problem with wide applications. Previous MkCP algorithms only work on small-scale instances and are not applicable for large-scale instances. For solving instances with different scales, this paper develops an efficient local search algorithm named NukCP for the MkCP which mainly includes two novel ideas. First, we propose a dynamic reduction strategy, which makes a good balance between the time efficiency and the precision effectiveness of the upper bound calculation. Second, a stratified threshold configuration checking strategy is designed by giving different priorities for the neighborhood in the different levels. Experiments on a broad range of different scale instances show that NukCP significantly outperforms the state-of-the-art MkCP algorithms on most instances in terms of solution quality.

Authors

• Jiejiang Chen
• Yiyuan Wang Northeast Normal University
• Shaowei Cai Institute of Software, Chinese Academy of Sciences
• Minghao Yin
• Yupeng Zhou Northeast Normal University
• Jieyu Wu

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