NukCP: An Improved Local Search Algorithm for Maximum k-Club Problem
Jiejiang Chen, Yiyuan Wang, Shaowei Cai, Minghao Yin, Yupeng Zhou, Jieyu Wu
Abstract
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.
BibTeX
@inproceedings{aaai2022_nukcpanimprovedl,
title = {NukCP: An Improved Local Search Algorithm for Maximum k-Club Problem},
author = {Jiejiang Chen and Yiyuan Wang and Shaowei Cai and Minghao Yin and Yupeng Zhou and Jieyu Wu},
booktitle = {AAAI 2022},
year = {2022}
}