On the use of multi neighbourhood structures within a Tabu-based memetic approach to university timetabling problems

Finding a good university timetabling system is not a simple task for a higher educational organisation. As a result, many approaches to generating sufficiently good solutions have been introduced. This is mainly due to the high complexity within the search landscape; moreover, each educational organisation has its own rules and specifications. In this paper, a Tabu-based memetic algorithm that hybridises a genetic algorithm with a Tabu Search algorithm is proposed as an improved algorithm for university timetabling problems. This algorithm is employed on a set of neighbourhood structures during the search process with the aim of gaining significant improvements in solution quality. The sequence of neighbourhood structures has been considered to understand its effect on the search space. Random, best and general sequences of neighbourhood structures have been evaluated in this work. The concept of a Tabu list is embedded to control the selection of neighbourhood structures that are not dependent on the problem domains during the optimisation process after the crossover and mutation operators are applied to the selected solutions from the population pool. The algorithm will penalise neighbourhood structures that are unable to generate better solutions. The proposed algorithm has been applied and evaluated against the latest methodologies in the literature with respect to standard benchmark problems. We demonstrate that the proposed algorithm produces some of the best known results when tested on ITC2007 competition datasets.