In this paper, we propose a new algorithm to solve university course timetabling problems using a Particle Swarm Optimization (PSO). PSOs are being increasingly applied to obtain near-optimal solutions to many numerical optimization problems. However, it is also being increasingly realized that PSOs do not solve constraint satisfaction problems as well as other meta-heuristics do. In this paper, we introduce transition probability into PSO to settle this problem. Experiments using timetables of the University of Tsukuba showed that this approach is a more effective solution than an Evolution Strategy. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kanoh, H., & Chen, S. (2013). Particle swarm optimization with transition probability for timetabling problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7824 LNCS, pp. 256–265). Springer Verlag. https://doi.org/10.1007/978-3-642-37213-1_27
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