Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a significant reduction of computational time. In this work, a GRASP-based state-of-the-art heuristic for the Minimum Latency Problem (MLP) is improved by means of data mining techniques for two MLP variants. Computational experiments showed that the approaches with data mining were able to match or improve the solution quality for a large number of instances, together with a substantial reduction of running time. In addition, 88 new cost values of solutions are introduced into the literature. To support our results, tests of statistical significance, impact of using mined patterns, equal time comparisons and time-to-target plots are provided.

翻译：近年来，混合元启发式算法已成为运筹学领域的发展趋势。一个成功的范例是将贪心随机自适应搜索过程与数据挖掘技术相结合——通过挖掘高质量解中的频繁模式，既能高效探索搜索空间，又能显著降低计算时间。本研究针对最小时延问题的两种变体，采用数据挖掘技术改进了基于GRASP的当前最优启发式算法。计算实验表明，引入数据挖掘的方法在大量算例中能够匹配或提升解的质量，同时大幅缩短运行时间。此外，本文为文献贡献了88个新的解成本值。为验证结果的可靠性，我们提供了统计显著性检验、挖掘模式使用效果分析、等时间对比实验以及时间-目标曲线图。