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Hybridization of GRASP with Exterior Path Relinking for Identifying Critical Nodes in Graphs

机译:GRASP与外部路径重新链接的杂交,用于识别图中的关键节点

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This paper deals a problem with detecting the critical nodes in order to achieve minimum pair-wise connectivity in the residual graph after removing a limited number of nodes from the graph. It is called the critical node detection problem (CNDP). In this paper, we propose a new hybridization scheme of greedy randomized adaptive search procedure (GRASP) with exterior path-relinking. Exterior path-relinking, a recently proposed metaheuristic method, creates paths of successive solutions starting from two high-quality solutions to other high quality solutions. A randomly selected solution from the elite solution pool collecting throughout GRASP iterations and the resulting solution from each GRASP iteration are relinked by exterior path-relinking for finding better solutions. The proposed algorithm is assisted on test instances from the literature. Computational experiments demonstrate that the proposed method outperforms other existing algorithms in the area of CNDP.
机译:本文提出了一个问题,即从图中删除有限数量的节点后,为了在残差图中实现最小的成对连通性而检测关键节点。这称为关键节点检测问题(CNDP)。在本文中,我们提出了一种新的贪婪随机自适应搜索过程(GRASP)与外部路径重新链接的混合方案。外部路径重新链接是一种最近提出的元启发式方法,它创建了从两个高质量解决方案到其他高质量解决方案的连续解决方案的路径。通过外部路径重新链接从精英解决方案池中随机选择的解决方案收集了整个GRASP迭代,并从每个GRASP迭代获得了最终解决方案,以寻找更好的解决方案。文献中的测试实例辅助了所提出的算法。计算实验表明,所提出的方法优于CNDP领域中的其他现有算法。

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