Modified and hybridised bi-objective firefly algorithms for university course scheduling

Academic institutions may be edging towards a global uncertainty, recession, and a string of financial difficulties. An effective course timetabling is one of managerial strategies to optimise the operating costs and resource utilisation. This paper presents the first application of firefly algorithm (FA) and its modifications and hybridisations for solving real-world course timetabling problem. A novel bi-objective firefly algorithm (BOFA) with Pareto dominance approach was developed for optimising the operating costs and resource utilisation. Random key technique was applied for adapting the continuous firefly movement to solve discrete timetabling problem. Five constructive heuristics were additionally embedded into the BOFA for initialising feasible timetables. Computational experiments were sequentially conducted using eleven problem instances obtained from a collaborating university. It was found that the proposed hybridisation outperformed particle swarm optimisation, the classical FA, and modified FA (MFA), in terms of the quality of the timetables obtained, computational times and convergent speed. The proposed hybrid bi-objective FA (HBOFA) yielded better Pareto frontiers than the conventional BOFA with less computational times for all problem instances.