Adapting And Hybridising Harmony Search With Metaheuristic Components For University Course Timetabling
University Course Timetabling Problem (UCTP) is a hard combinatorial scheduling problem. Harmony Search Algorithm (HSA) is a recent metaheuristic population-based method. The major thrust of this algorithm lies in its ability to integrate the key components of populationbased methods and local se...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 語言: | English |
| 出版: |
2010
|
| 主題: | |
| 在線閱讀: | http://eprints.usm.my/41659/1/MOHAMMED_AZMI_AL-BETAR_HJ.pdf |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | University Course Timetabling Problem (UCTP) is a hard combinatorial scheduling problem.
Harmony Search Algorithm (HSA) is a recent metaheuristic population-based method.
The major thrust of this algorithm lies in its ability to integrate the key components of populationbased
methods and local search-based methods in the same optimisation model. This dissertation
presents a HSA adapted for UCTP. The adaptation involved modifying the HSA operators.
The results were within the range of state of the art. However, some shortcomings in the convergence
rate and local exploitation were identified and addressed through hybridisation with
known metaheuristic components. Three hybridized versions are proposed which are incremental
improvements over the preceding version: (i) Modified Harmony Search Algorithm
(MHSA); (ii) Harmony Search Algorithm with Multi-Pitch Adjusting Rate (HSA-MPAR), and
(iii) Hybrid Harmony Search Algorithm (HHSA). The results were compared against 21 other
methods using eleven de facto standard dataset of different sizes and complexity. The proposed
hybridized versions achieved the optimal solution for the small datasets, with two best overall
results for the medium datasets. Furthermore, in the large and most complex dataset the
proposed hybrid methods achieved the best result. |
|---|