Iterated local search using an add and delete hyper-heuristic for university course timetabling
Iterated local search using an add and delete hyper-heuristic for university course timetabling
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DOI：
10.1016/j.asoc.2015.11.043
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
J. Soria-Alcaraz;E. Özcan;J. Swan;G. Kendall;Juan Martín Carpio Valadez
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文献类型：
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作者：
J. Soria-Alcaraz;E. Özcan;J. Swan;G. Kendall;Juan Martín Carpio Valadez
超级算法是在更高级别上操作以选择或生成一组低级（Meta）算法以试图解决困难的优化问题的（Meta）算法。迭代局部搜索（ILS）是一种众所周知的离散优化方法，在迭代框架内结合了扰动和爬山。在这项研究中，我们介绍了ILS的方法，加强了超启发式生成的基础上，一个固定数量的添加和删除操作的算法。所提出的超启发式的性能测试在两个不同的问题域使用真实的世界基准的课程编排实例从第二届国际排课比赛轨道2和3。结果表明，在ILS框架内混合添加和删除操作产生了有效的超启发式方法。
Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.