Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search
AbstractCourse timetabling is a combinatorial optimization problem and has been confirmed to be an NP-complete problem. Course timetabling problems are different for different universities. The studied university course timetabling problem involves hard constraints such as classroom, class curriculum, and other variables. Concurrently, some soft constraints need also to be considered, including teacher’s preferred time, favorite class time etc. These preferences correspond to satisfaction values obtained via questionnaires. Particle swarm optimization (PSO) is a promising scheme for solving NP-complete problems due to its fast convergence, fewer parameter settings and ability to fit dynamic environmental characteristics. Therefore, PSO was applied towards solving course timetabling problems in this work. To reduce the computational complexity, a timeslot was designated in a particle’s encoding as the scheduling unit. Two types of PSO, the inertia weight version and constriction version, were evaluated. Moreover, an interchange heuristic was utilized to explore the neighboring solution space to improve solution quality. Additionally, schedule conflicts are handled after a solution has been generated. Experimental results demonstrate that the proposed scheme of constriction PSO with interchange heuristic is able to generate satisfactory course timetables that meet the requirements of teachers and classes according to the various applied constraints. View Full-Text
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Chen, R.-M.; Shih, H.-F. Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search. Algorithms 2013, 6, 227-244.
Chen R-M, Shih H-F. Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search. Algorithms. 2013; 6(2):227-244.Chicago/Turabian Style
Chen, Ruey-Maw; Shih, Hsiao-Fang. 2013. "Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search." Algorithms 6, no. 2: 227-244.