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Breakout Local Search for the Max-Cut problem

机译:突破本地搜索的最大剪切问题

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摘要

Given an undirected graph C = (V,£) where each edge of £ is weighted with an integer number, the maximum cut problem (Max-Cut) is to partition the vertices of V into two disjoint subsets so as to maximize the total weight of the edges between the two subsets. As one of Karp's 21 NP-complete problems, Max-Cut has attracted considerable attention over the last decades. In this paper, we present Breakout Local Search (BLS) for Max-Cut. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. The proposed algorithm shows excellent performance on the set of well-known maximum cut benchmark instances in terms of both solution quality and computational time. Out of the 71 benchmark instances, BLS is capable of finding new improved results in 34 cases and attaining the previous best-known result for 35 instances, within computing times ranging from less than 1 s to 5.6 h for the largest instance with 20,000 vertices.
机译:给定一个无向图C =(V,£),其中£的每个边都用整数加权,最大分割问题(​​Max-Cut)是将V的顶点划分为两个不相交的子集,以使总权重最大化两个子集之间的边缘。作为Karp的21个NP完全问题之一,Max-Cut在过去几十年中引起了相当大的关注。在本文中,我们介绍了Max-Cut的突围局部搜索(BLS)。 BLS通过结合使用局部搜索和自适应摄动策略来探索搜索空间。所提出的算法在解决方案质量和计算时间方面,在一组著名的最大割基准实例上均表现出出色的性能。在71个基准实例中,BLS能够在34个案例中找到新的改进结果,并在35个实例中获得以前最著名的结果,对于具有20,000个顶点的最大实例,计算时间范围从不到1 s到5.6 h。

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