An adapted cuckoo optimization algorithm and genetic algorithm approach to the university course timetabling problem

Abstract

The university course timetabling problem is an NP-hard and NP-complete problem concerned with assigning a specific set of events and resources to timeslots under a highly-constrained search space. This paper presents a novel metaheuristic algorithm entitled adapted cuckoo optimization algorithm which is derived from the cuckoo optimization algorithm and cuckoo search algorithm. This algorithm includes features such as local random walk on discrete data which mimics the behavior of Lévy flights and an Elitism-based mechanism which echoes back the best candidate solutions and prevents the algorithm from plunging into a curse of dimensionality. The algorithm was tested on a problem instance gathered from a University in Malaysia and the results indicate that the proposed algorithm exhibits very promising results in terms of solution quality and computational speed when compared to genetic algorithms