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Competition-guided multi-neighborhood local search algorithm for the university course timetabling problem
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-06-19 , DOI: 10.1016/j.asoc.2021.107624
Ting Song , Mao Chen , Yulong Xu , Dong Wang , Xuekun Song , Xiangyang Tang

This paper proposes a novel competition-guided multi-neighborhood local search (CMLS) algorithm for solving the curriculum-based course timetabling problem. In comparison with the classical metaheuristic methods in the literature, the proposed algorithm includes three main contributions. Firstly, a new way of combining multiple neighborhoods is presented, according to which, only one neighborhood is selected at each iteration to make a trade-off between large search space and high computational efficiency. Secondly, two heuristic rules are proposed to determine the probabilities of selecting the neighborhood. Thirdly, a competition-based restart strategy is proposed, i.e., two SA-based multi-neighborhood local search procedures are implemented during the search process, and the elite one is selected from the two results obtained by the two procedures as the initial solution for the next round of search. The proposed algorithm is evaluated on the well-known benchmark instances, and the computational results show that the proposed algorithm is highly competitive compared with 6 state-of-the-art algorithms in the literature. Analysis of the essential ingredients of the proposed algorithm is also presented.



中文翻译:

大学课程时间表问题的竞争引导多邻域局部搜索算法

本文提出了一种新颖的竞争引导多邻域局部搜索(CMLS)算法来解决基于课程的课程安排问题。与文献中的经典元启发式方法相比,所提出的算法包括三个主要贡献。首先,提出了一种新的多邻域组合方式,即每次迭代只选择一个邻域,在大搜索空间和高计算效率之间进行权衡。其次,提出了两个启发式规则来确定选择邻域的概率。第三,提出了一种基于竞争的重启策略,即在搜索过程中执行两个基于SA的多邻域局部搜索程序,并从两个程序得到的两个结果中选出精英一个作为下一轮搜索的初始解。在众所周知的基准实例上对所提出的算法进行了评估,计算结果表明,与文献中的 6 种最先进算法相比,所提出的算法具有很强的竞争力。还介绍了对所提出算法的基本成分的分析。

更新日期:2021-06-21
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