论文标题
部分可观测时空混沌系统的无模型预测
Multilevel Memetic Hypergraph Partitioning with Greedy Recombination
论文作者
论文摘要
HyperGraph分区（HGP）问题是一个经过充分研究的问题，可以在各种域中找到应用。有关HGP问题的文献已重点关注发展快速的启发式方法。在几个应用程序域（例如VLSI设计和数据库迁移计划）中，解决方案的质量比算法的运行时间更关注。 KAHYPAR-E是第一个为HGP问题设计的多层次模因算法，如果给出了足够的计算时间，它与启发式算法相比，它返回更好的质量解决方案。在这项工作中，我们介绍了新颖的问题特异性重组和突变算子，并通过将KAHYPAR-E与这些操作员相结合来开发一种新的多层次模因算法。将我们算法的性能与最先进的HGP算法进行了比较，$ 150 $的现实生活实例从文献中使用的基准数据集中获取。在实验中，这将花费39,000美元的单核计算机，每种算法都会给出$ 2、4 $和8美元的$ 8 $小时，以计算每个实例的解决方案。我们的算法胜过所有其他算法，并分别以$ 112 $，$ 115 $和$ 125 $ $ 2、4 $和$ 8 $小时的价格找到最佳解决方案。
The Hypergraph Partitioning (HGP) problem is a well-studied problem that finds applications in a variety of domains. The literature on the HGP problem has heavily focused on developing fast heuristic approaches. In several application domains, such as the VLSI design and database migration planning, the quality of the solution is more of a concern than the running time of the algorithm. KaHyPar-E is the first multilevel memetic algorithm designed for the HGP problem and it returns better quality solutions, compared to the heuristic algorithms, if sufficient computation time is given. In this work, we introduce novel problem-specific recombination and mutation operators, and develop a new multilevel memetic algorithm by combining KaHyPar-E with these operators. The performance of our algorithm is compared with the state-of-the-art HGP algorithms on $150$ real-life instances taken from the benchmark datasets used in the literature. In the experiments, which would take $39,000$ hours in a single-core computer, each algorithm is given $2, 4$, and $8$ hours to compute a solution for each instance. Our algorithm outperforms all others and finds the best solutions in $112$, $115$, and $125$ instances in $2, 4$, and $8$ hours, respectively.
