The maximum k-plex problem is an important, computationally complex graph based problem. In this study an effective k-plex local search (KLS) is presented for solving this problem on a wide range of graph types. KLS uses data structures suitable for the graph being analysed and has mechanisms for preventing search cycling and promoting search diversity. State of the art results were obtained on 121 dense graphs and 61 large real-life (sparse) graphs. Comparisons with three recent algorithms on the more difficult graphs show that KLS performed better or as well as in 93% of 332 significant k-plex problem instances investigated achieving either larger average k-plex sizes (including some new results) or, when these were equivalent, lower CPU requirements.

最大k丛问题是一个重要的，基于计算复杂图的问题。在这项研究中，提出了一种有效的k -plex局部搜索（KLS）来解决各种图类型上的问题。KLS使用适合于所分析图形的数据结构，并具有防止搜索循环和促进搜索多样性的机制。在121张密集图和61张大型现实（稀​​疏）图上获得了最新技术结果。在较困难的图上与三种最新算法的比较表明，在研究的332个重要k复杂问题实例中，KLS的性能更好或达到93％，或者达到了更大的平均k-plex大小（包括一些新结果），或者当它们相等时，降低CPU要求。