Abstract
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.
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Kanoh, H., Chen, S. (2013). Particle Swarm Optimization with Transition Probability for Timetabling Problems. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_27
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DOI: https://doi.org/10.1007/978-3-642-37213-1_27
Publisher Name: Springer, Berlin, Heidelberg
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