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Building Highly Reliable Networks with GRASP/VND Heuristics

机译:利用GRASP / VND启发式方法构建高度可靠的网络

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There is a strong interplay between network reliability and connectivity theory. In fact, previous studies show that the graphs with maximum reliability, called uniformly most-reliable graphs, must have the highest connectivity. In this paper, we revisit the underlying theory in order to build uniformly most-reliable cubic graphs. The computational complexity of the problem promotes the development of heuristics. The contributions of this paper are three-fold. In a first stage, we propose an ideal Variable Neighborhood Descent (VND) which returns the graph with maximum reliability. This VND works in exponential time. In a second stage, we propose a Greedy Randomized Adaptive Search Procedure (GRASP), that trades quality for computational effort. A construction phase enriched with a Restricted Candidate List (RCL) offers diversification. Our local search phase includes a globally optimum solution of an Integer Linear Programming (ILP) formulation. As a product of our research, we recovered previous optimal graphs from the related literature in the field. Additionally, we offer new candidates of uniformly most-reliable graphs with maximum connectivity and maximum number of spanning trees.
机译:网络可靠性和连接性理论之间有着很强的相互作用。实际上,先前的研究表明,具有最高可靠性的图(称为统一最可靠的图)必须具有最高的连通性。在本文中,我们将重新研究基础理论,以便构建统一的最可靠的立方图。问题的计算复杂性促进了启发式方法的发展。本文的贡献是三方面的。在第一阶段,我们提出了一种理想的可变邻域下降(VND),它以最大的可靠性返回图。此VND以指数时间工作。在第二阶段,我们提出了一种贪婪的随机自适应搜索过程(GRASP),该过程以质量为代价进行计算。丰富的受限候选清单(RCL)的建设阶段提供了多样化的解决方案。我们的本地搜索阶段包括整数线性规划(ILP)公式的全局最优解决方案。作为我们研究的结果,我们从该领域的相关文献中恢复了以前的最优图。此外,我们为统一性最可靠的图提供了新的候选对象,这些图具有最大的连通性和最大的生成树数。

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