Adaptive Diversifying Hyper-Heuristic Based Approach for Timetabling Problems

Combinatorial optimization is the search for an optimal configuration of a set of variables to accomplish certain goals. One of the well-known combinatorial optimization problems is the timetabling problem, with a lot of research conducted in the past few decades to investigate a variety of methodologies to solve it. One of the blossoming recent methodologies is hyper-heuristics, which attempts to automate the algorithm design process so that it would be able to work with different sets of problem domains. This paper focuses on the university course timetabling problem (UCTP) as the case of study, and proposes the use of a competitive iterated local search approach strengthened with an add-delete hyper-heuristic. The hyper-heuristic utilizes an adaptive heuristic generation mechanism through a variable-sized list of add and delete operations. The algorithm was enhanced with the use of a novel approach to construct a good feasible initial solution and strengthened with a diversifying mechanism to allow more exploration over large search spaces to find a global rather than local near optimal solution. The proposed work was tested with the ITC2007 benchmark datasets, and experiments show promising results and give better average performance when compared to recent approaches in the literature that work on similar timetabling problems.