University course timetable planning using hybrid particle swarm optimization

Abstract

University Course Timetabling (UCT) is a complex problem and cannot be dealt with using only a few general principles. The complicated relationships between time periods, subjects and classrooms make it difficult to obtain feasible solution. Thus, finding feasible solution for UCT is a continually challenging problem. This paper presents a hybrid particle swarm optimization algorithm to solve University Course Timetabling Problem (UCTP). The proposed approach (hybrid particle swarm optimization with constraint-based reasoning) uses particle swarm optimization to find the position of room and timeslot using suitable objective function and the constraints-based reasoning has been used to search for the best preference value based on the student capacity for each lesson in a reasonable computing time. The proposed algorithm has been validated with other hybrid algorithms (hybrid particle swarm optimization with local search and hybrid genetic algorithm with constraint-based reasoning) using a real world data from Faculty of Science at Ibb University -- Yemen and results show that the proposed algorithm can provide more promising solution.