Iterated local search using an add and delete hyper-heuristic for university course timetabling |
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| Affiliation: | 1. Departamento de Estudios Organizacionales, División de Ciencias Economico Administrativas, Universidad de Guanajuato, Mexico;2. University of Nottingham, School of Computer Science Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK;3. York Centre for Complex Systems Analysis, University of York, UK;4. University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia;5. Tecnológico Nacional de México, Instituto Tecnológico de León, Mexico;1. Computing and Systems Department, Federal University of Ouro Preto, Brazil;2. Computing Department, Federal University of Ouro Preto, Brazil;1. School of Science and Technology, Hellenic Open University, Parodos Aristotelous 18, 26335 Patra, Greece;2. Department of Business Administration of Food and Agricultural Enterprises, University of Patras, G. Seferi 2, GR-30100 Agrinio, Greece;1. Department of Computer Engineering, College of Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran;2. Department of Computer Sciences, University of Tabriz, Tabriz, Iran;3. Department of Mechanical Engineering, College of Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran;1. Laboratory of Modeling and Optimization for Decisional, Industrial and Logistic Systems (MODILS), Faculty of Economics and Management of Sfax, Tunisia;2. Université de Lyon, F-42023, Saint Etienne, France;3. Université de Saint Etienne, Jean Monnet, F-42000, Saint-Etienne, France;4. LASPI, F-42334, IUT de Roanne |
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| 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. |
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| Keywords: | Hyper-heuristic Iterated local search Add–delete list Methodology of design Educational timetabling |
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