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首页> 外文期刊>European Journal of Operational Research >An improved configuration checking-based algorithm for the unicost set covering problem
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An improved configuration checking-based algorithm for the unicost set covering problem

机译:一种改进的基于配置检查的Unicost集合涵盖问题的算法

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Configuration Checking (CC) is a simple tool that can be added to local search algorithms to prevent cycling. The generic forms of CC and local search may not be suitable to solve large-scale unicost set covering problem (USCP) instances. Thus, in this study, we introduce an improved CC-based algorithm to solve USCPs. Unlike previous CC implementations that only consider subset states to prevent cycling, the proposed algorithm also checks the element states to minimize the number of subsets, in order to cut down unnecessary search spaces. Therefore, we refer to this technique as the element-state configuration checking (ES-CC) algorithm. Moreover, in our proposed algorithm, the score value (a numerical measure to differentiate between subsets) considers multiple levels of element covering. This multi-level scoring (MLS) value is a new powerful contribution compared to the single-level scoring used in previous CC algorithms. Using these two novel ideas, MLS and ES-CC, we implement the new MLSES-CC algorithm to solve the USCP. The MLSES-CC algorithm also implements a more aggressive local search routine that simultaneously changes the status of the three subsets. We use the MLSES-CC algorithm to solve 176 USCP instances that belong to standard and novel benchmarking sets and compare our results to the bestknown USCP algorithms, in terms of solution quality and computation time. Computational experiments indicate that the MLSES-CC algorithm can be considered as a new state-of-the-art algorithm to solve USCPs.
机译:配置检查(CC)是一个简单的工具,可以添加到本地搜索算法中,以防止循环。CC和局部搜索的一般形式可能不适合解决大规模unicost集合覆盖问题(USCP)实例。因此,在本研究中,我们引入了一种改进的基于CC的算法来解决USCPs。不同于以往的CC实现只考虑子集状态,以防止骑自行车,所提出的算法还检查元素状态,以尽量减少子集的数量,以减少不必要的搜索空间。因此,我们将这种技术称为元素状态配置检查(ES-CC)算法。此外,在我们提出的算法中,分数值(区分子集的数值度量)考虑了多个元素覆盖级别。与以前的CC算法中使用的单级评分相比,这个多级评分(MLS)值是一个新的强大贡献。利用MLS和ES-CC这两种新思想,我们实现了新的MLSES-CC算法来解决USCP问题。MLSES-CC算法还实现了一个更具攻击性的局部搜索例程,该例程可以同时更改三个子集的状态。我们使用MLSES-CC算法解决了176个USCP实例,这些实例属于标准和新的基准测试集,并在解决方案质量和计算时间方面将我们的结果与最知名的USCP算法进行了比较。计算实验表明,MLSES-CC算法可以被认为是解决USCPs问题的一种新的先进算法。

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