Intensification-driven tabu search for the minimum differential dispersion problem

Intensification-driven tabu search for the minimum differential dispersion problem
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集约化驱动的禁忌搜索最小差分色散问题

DOI:
10.1016/j.knosys.2019.01.010
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发表时间:
2019-03
影响因子:
8.8
通讯作者:
Yue Dong
Yue Dong
中科院分区:
计算机科学1区
文献类型:
--
作者:
Lai Xiangjing;Hao Jin Kao;Glover Fred;Yue Dong

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最小差分色散问题是一个具有许多相关应用的NP-hard组合优化问题。在本文中,我们提出了一种强化驱动的禁忌搜索算法,通过整合约束邻域、基于解的禁忌策略和强化搜索机制来创建有效利用强化和多样化元素的搜索,来解决这个计算上具有挑战性的问题。我们通过对250个基准实例中的127个(bbb50 %)提出改进的新最佳解决方案来证明所提出算法的竞争力。通过研究算法的搜索轨迹来揭示算法的行为,研究高质量解在搜索空间中的空间分布,以激励强化搜索机制的设计选择。
The minimum differential dispersion problem is a NP-hard combinatorial optimization problem with numerous relevant applications. In this paper, we propose an intensification-driven tabu search algorithm for solving this computationally challenging problem by integrating a constrained neighborhood, a solution-based tabu strategy, and an intensified search mechanism to create a search that effectively exploits the elements of intensification and diversification. We demonstrate the competitiveness of the proposed algorithm by presenting improved new best solutions for 127 out of 250 benchmark instances (> 50%). We study the search trajectory of the algorithm to shed light on its behavior and investigate the spatial distribution of high-quality solutions in the search space to motivate the design choice of the intensified search mechanism.
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