Iterated local search using an add and delete hyper- heuristic for university course timetabling

Soria-Alcaraz, Jorge A.; �zcan, Ender; Swan, Jerry; Kendall, Graham; Carpio, Martin

Abstract

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.

Citation

Soria-Alcaraz, J. A., Özcan, E., Swan, J., Kendall, G., & Carpio, M. (2016). Iterated local search using an add and delete hyper- heuristic for university course timetabling. Applied Soft Computing, 40, https://doi.org/10.1016/j.asoc.2015.11.043

• Journal Article Type: Article
• Publication Date: Mar 1, 2016
• Deposit Date: Mar 10, 2016
• Publicly Available Date: Aug 9, 2018
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 40
• DOI: https://doi.org/10.1016/j.asoc.2015.11.043
• Keywords: Benchmarking; Heuristic methods; Iterative methods; Local search (optimization); Scheduling, Add-delete list; Discrete optimization; Hyperheuristic; Iterated local search; Iterative framework; Optimization problems; Timetabling; University Course Timetabling, Optimization
• Public URL: https://nottingham-repository.worktribe.com/output/978148
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1568494615007760