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Journal of Applied Sciences

Year: 2013 | Volume: 13 | Issue: 7 | Page No.: 1087-1093
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Authors

H.Y. Tarawneh

Masri Ayob

Keywords

Research Article

Adaptive Neighbourhoods Structure Selection Mechanism in Simulated Annealing for Solving University Course Timetabling Problems

H.Y. Tarawneh and Masri Ayob
Usually, meta-heuristic approaches that use several neighbourhood structures can perform better than single neighbourhood structure. However, choosing a suitable neighbourhood structure to be applied during the search process is also a crucial decision. Therefore, this study proposes an adaptive neighbourhoods structure selection (AD-NS) mechanism that adaptively memorised the improvement strengths for each neighbourhood structure. The neighbourhood structure with the best improvement history will be employed to generate neighbour(s) for the current iteration. The hypothesis is, “if the improvement history of neighbourhood structure can affect the performance of neighbourhood structures selection mechanism and subsequently, the performance the applied meta-heuristic, then the meta-heuristic (i.e., Simulated Annealing (SA) in this case) with AD-NS will outperform the meta-heuristic (i.e., SA) with other neighbourhood structure selection mechanisms”. To prove this, the experiment is conducted by applying SA with AD-NS, SA with Token ring and SA with Union neighbourhood structure selection mechanisms; tested on Curriculum-Based Course Timetabling problem for the ITC-2007 track3 benchmark datasets. Results based on the average ranked, shows that SA with AD-NS approach obtained the fourth rank compared with other approaches reported in the literature. Statistical analysis on SA with AD-NS against SA with other neighbourhood structure selection mechanisms proved that the performance of SA with AD-NS is significantly better than SA with other neighbourhood structures selection mechanisms tested in this work. This indicates that the improvement history of neighbourhood structure can affect the performance of neighbourhood structures selection mechanism and subsequently, the performance the applied meta-heuristic.
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