关键节点问题的增量式评估机制
An Incremental Evaluation Mechanism for the Critical Node Problem
摘要：
关键节点问题（CNP）是指在一个图中找出一个节点子集，该子集的删除最大程度地降低了两两连通性。CNP是临界节点检测问题的一个重要变种，有着广泛的应用。由于其NP-困难的一般图，大多数工作集中在局部搜索算法，可以返回一个良好的质量解决方案在一个合理的时间。然而，计算CNP的目标函数是一个频繁的过程，并且是耗时的（复杂度为O（|V| + |E|）），这是以前算法所遇到的一个常见问题。在本文中，我们提出了一个通用的增量评估机制（IEM）计算的目标函数具有更低的复杂性。在这项工作中，我们改进了两个重要的贪婪操作与IEM，以及实验。最后，我们评估IEM应用到进化算法的两个流行的基准，与国家的最先进的方法相比。实验结果表明了IEM的重要性。
英文摘要：
The Critical Node Problem (CNP) is to identify a subset of nodes in a graph whose removal maximally degrades pairwise connectivity. The CNP is an important variant of the Critical Node Detection Problem (CNDP) with wide applications. Due to its NP-hardness for general graphs, most works focus on local search algorithms that can return a good quality solution in a reasonable time. However, computing the objective function of CNP is a frequent procedure and is time-consuming (with complexity O(|V | + |E|)) during the search, which is a common problem that previous algorithms suffered from. In this paper, we propose a general incremental evaluation mechanism (IEM) to compute the objective function with much lower complexity. In this work, we improved two important greedy operations with IEM, along with experiments. Finally, we evaluate IEM by applying it into an evolutionary algorithm on two popular benchmarks, compared with the state-of-the-art approach. The experimental results showed the significance of IEM.
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