Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid
Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. Previous studies have shown that HSA has been successfully adapted f...
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| Main Author: | |
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| Format: | Book Section |
| Language: | English |
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Institute of Graduate Studies, UiTM
2017
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| Online Access: | http://ir.uitm.edu.my/id/eprint/19762/1/ABS_JULIANA%20WAHID%20TDRA%20VOL%2011%20IGS%2017.pdf http://ir.uitm.edu.my/id/eprint/19762/ |
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| Summary: | Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. Previous studies have shown that HSA has been successfully adapted for solving combinatorial optimization problems such as university course timetabling problem (UCTP). However, HSA encountered a setback in which the convergence rate and accuracy of the obtained results are reduced because of the solutions in the population are eventually about the same during the final iterations. Thus, this thesis proposed hybrid algorithms between HSA and local search based methods (simulated annealing (SA) and/or great deluge (GD)) to enhance the HSA performance for solving curriculum-based course timetabling (CBCTT) problem which is the variant of UCTP. SA is chosen to be hybridize with HSA for solving CBCTT because in literature, SA was successfully hybridize with HSA to solve other domain of problems. GD is chosen to be hybridize with HSA for solving CBCTT because GD has the related procedure with SA… |
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