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

Graphical abstractDisplay Omitted HighlightsAdd and delete operations are encoded as a list/string of integers (ADL).An effective hyper-heuristic approach operating with ADLs is proposed.Low level heuristics perform search over the space of feasible solutions.Proposed approach produces new best solutions to some instances.Proposed approach achieves generality across two variants of the timetabling problem. 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.