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A threshold search based memetic algorithm for the disjunctively constrained knapsack problem
Computers & Operations Research ( IF 4.6 ) Pub Date : 2021-07-17 , DOI: 10.1016/j.cor.2021.105447
Zequn Wei 1 , Jin-Kao Hao 1
Affiliation  

The disjunctively constrained knapsack problem consists in packing a subset of pairwisely compatible items in a capacity-constrained knapsack such that the total profit of the selected items is maximized while satisfying the knapsack capacity. DCKP has numerous applications and is however computationally challenging (NP-hard). In this work, we present a threshold search based memetic algorithm for solving the DCKP that combines the memetic framework with threshold search to find high quality solutions. Extensive computational assessments on two sets of 6340 benchmark instances in the literature demonstrate that the proposed algorithm is highly competitive compared to the state-of-the-art methods. In particular, we report 24 and 354 improved best-known results (new lower bounds) for Set I (100 instances) and for Set II (6240 instances), respectively. We additionally apply the approach to solve a real-life daily photograph scheduling problem of an earth observation satellite. We analyze the key algorithmic components and shed lights on their roles for the performance of the algorithm.



中文翻译:

基于阈值搜索的分离约束背包问题的模因算法

分离约束背包问题包括在容量约束背包中包装成对兼容物品的子集,以便在满足背包容量的同时使所选物品的总利润最大化。DCKP 有许多应用,但在计算上具有挑战性(NP 难)。在这项工作中,我们提出了一种基于阈值搜索的模因算法来解决 DCKP,该算法将模因框架与阈值搜索相结合,以找到高质量的解决方案。对文献中两组 6340 个基准实​​例的广泛计算评估表明,与最先进的方法相比,所提出的算法具有很强的竞争力。特别是,我们报告了集 I(100 个实例)和集 II(6240 个实例)的 24 和 354 个改进的最佳已知结果(新的下限),分别。我们还应用该方法来解决地球观测卫星的实际日常照片调度问题。我们分析了关键的算法组件并阐明了它们对算法性能的作用。

更新日期:2021-07-21
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