Abstract Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times.

中文翻译：

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MineReduce：一种基于数据挖掘的减少问题规模的方法

摘要 包括数据挖掘策略在内的元启发式混合变体已被用于解决各种组合优化问题，并取得了令人鼓舞的结果。以前的混合策略应用挖掘模式来指导初始解决方案的构建，从而更有效地探索解决方案空间。解决组合优化问题通常是一项艰巨的任务，因为其解决方案空间随其大小呈指数增长。因此，在这种情况下，减少问题规模也是一种有用的策略，尤其是在大规模问题的情况下。在本文中，我们通过提出一种名为 MineReduce 的方法建立在这些想法的基础上，该方法使用挖掘的模式来执行问题规模缩减。我们提出了 MineReduce 的应用，以改进异构车队车辆路径问题的启发式方法。在计算实验中获得的结果表明，与原始启发式和其他最先进的启发式方法相比，这种提出的启发式方法表现出优越的性能，以更短的运行时间实现更好的解决方案成本。