Abstract
We propose an tabu search algorithm using an candidate list stratety with random sampling for the university course timetabling problem, where the neighborhood size can be adjusted by a parameter ratio. With this framework, we can control the trade-off between exploration and exploitation by adjusting the neighborhood size. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms when the neighborhood size is set properly.
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Nagata, Y., Ono, I. (2014). Random Partial Neighborhood Search for University Course Timetabling Problem. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_77
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DOI: https://doi.org/10.1007/978-3-319-10762-2_77
Publisher Name: Springer, Cham
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