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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Tendio, Yusnardo
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Computer Science & IT Engineering

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The performance analysis of hyper-heuristics algorithms over examination timetabling problems Muklason, Ahmad; Tendio, Yusnardo; Angelita Depari, Helena; Arif Nuriman, Muhammad; Agung Premananda, I Gusti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2155-2164

Abstract

In general, uncapacitated exam timetabling is conducted manually, which can be time-consuming. Many studies aim to automate and optimize uncapacitated exam timetabling. However, pinpointing the most efficient algorithm is challenging since most studies assert that their algorithms surpass previous ones. To identify the optimal algorithm, this research evaluates the performance of four algorithms: Hill climbing (HC), simulated annealing (SA), great deluge (GD), and tabu search (TS) in addressing the exam timetabling problem. The Kempe chain operator’s influence on optimization solutions is also examined. A simple random method is employed to select the low-level heuristic (LLH). The Carter (Toronto) dataset served as the test material, with each algorithm undergoing 200,000 iterations for comparison. The results indicate that the TS algorithm is superior, providing the best solution in 13 instances. The use of a tabu list enhanced the search process’s efficiency by preventing redundant modifications. The Kempe chain LLH exhibited a tendency towards achieving better solutions.
Co-Authors Agung Premananda, I Gusti Ahmad Muklason Angelita Depari, Helena Arif Nuriman, Muhammad