局部思考，全局行动：完美平衡的图划分
Think Locally, Act Globally: Perfectly Balanced Graph Partitioning
摘要：
我们提出了一种新的局部改进方案的完美平衡图划分问题。该方案将不限于平衡约束的局部搜索编码到一个模型中，使我们能够找到这些搜索的组合，通过应用负循环检测算法来保持平衡。我们结合这种技术与算法来平衡不平衡的解决方案，并将其集成到一个并行的多级进化算法，KaFFPaE，来解决这个问题。总的来说，我们得到了一个系统，一方面是快速的，另一方面是能够改善或重现大部分的最知名的完美平衡的分区结果在文献中报道。
英文摘要：
We present a novel local improvement scheme for the perfectly balanced graph partitioning problem. This scheme encodes local searches that are not restricted to a balance constraint into a model allowing us to find combinations of these searches maintaining balance by applying a negative cycle detection algorithm. 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 most of the best known perfectly balanced partitioning results ever reported in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
