The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.

中文翻译：

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NuSC：解决集合覆盖问题的有效局部搜索算法。

集合覆盖问题 (SCP) 是计算机科学中一个基本的 NP 难题，具有广泛的重要现实应用。在实践中，从现实世界应用程序转换而来的 SCP 实例将是大规模的，因此设计有效的启发式算法，尤其是局部搜索算法非常重要。然而，关于开发解决 SCP 的局部搜索算法的研究工作很少。在本文中，我们提出了一种新的用于解决 SCP 的局部搜索算法，称为 NuSC。特别是，NuSC 引入了新的子集选择组合评分功能，有效地结合了不同的子集属性，帮助 NuSC 找到更优化的解决方案。此外，NuSC 结合了元素的动态加权方案、禁忌搜索策略、以及一个新颖的选择机制，以进一步提高其实际性能。为了研究我们提出的 NuSC 算法的有效性和稳健性，我们进行了广泛的实验，以在各种类型的 SCP 实例上将 NuSC 与许多最先进的竞争对手进行比较。我们的实验结果表明，NuSC 在大多数情况下明显优于其竞争对手，表明 NuSC 的优越性。此外，我们的经验评估证实了 NuSC 基础的每种算法技术的有效性。我们的实验结果表明，NuSC 在大多数情况下明显优于其竞争对手，表明 NuSC 的优越性。此外，我们的经验评估证实了 NuSC 基础的每种算法技术的有效性。我们的实验结果表明，NuSC 在大多数情况下明显优于其竞争对手，表明 NuSC 的优越性。此外，我们的经验评估证实了 NuSC 基础的每种算法技术的有效性。