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Think Locally, Act Globally: Highly Balanced Graph Partitioning

机译:在本地思考,在全球范围内行动:高度平衡的图形分区

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We present a novel local improvement scheme for graph partitions that allows to enforce strict balance constraints. Using negative cycle detection algorithms this scheme combines local searches that individually violate the balance constraint into a more global feasible improvement. We combine this technique with an algorithm to balance unbalanced solutions and integrate it into a parallel multi-level evolutionary algorithm, KaFFPaE, to tackle the problem. Overall, we obtain a system that is fast on the one hand and on the other hand is able to improve or reproduce many of the best known perfectly balanced partitioning results reported in the Walshaw benchmark.
机译:我们为图形分区提供了一种新的本地改进方案,允许强制执行严格的平衡约束。使用负循环检测算法,该方案将本地搜索组合成单独违反余额约束进入更全球可行的改进。我们将这种技术与算法相结合以平衡不平衡的解决方案,并将其集成到并行多级进化算法,Kaffpae中,以解决问题。总的来说,我们获得了一方面快速的系统,另一方面,能够改善或重现许多最着名的最知名的完美平衡分区结果,在Walshaw基准中报告。

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