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The performance analysis of hyper-heuristics algorithms over examination timetabling problems

IAES International Journal of Artificial Intelligence · Vol. 13 No. 2 (2024) · hlm. 2155–2164

DOI
10.11591/ijai.v13.i2.pp2155-2164
ISSN
2252-8938, 2089-4872
Terbit
2024-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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Rekan penulisOrganisasiArtikel bersama
Edwin RiksakomaraInstitut Teknologi Sepuluh Nopember3
Retno Aulia VinartiInstitut Teknologi Sepuluh Nopember2
Faizal MahanantoInstitut Teknologi Sepuluh Nopember2
Raras TyasnuritaInstitut Teknologi Sepuluh Nopember2
Shof Rijal Ahlan RobbaniInstitut Teknologi Sepuluh Nopember2
Anang FirdausInstitut Teknologi Sepuluh Nopember1
Vicha Azthanty SupoyoInstitut Teknologi Sepuluh Nopember1
Muhammad Reza PahlawanInstitut Teknologi Sepuluh Nopember1
Wiwik AnggraeniInstitut Teknologi Sepuluh Nopember1
Mursyidatun NabilahInstitut Teknologi Sepuluh Nopember1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">The performance analysis of hyper-heuristics algorithms over examination timetabling problems</dc:title>
	<dc:creator>Muklason, Ahmad</dc:creator>
	<dc:creator>Tendio, Yusnardo</dc:creator>
	<dc:creator>Angelita Depari, Helena</dc:creator>
	<dc:creator>Arif Nuriman, Muhammad</dc:creator>
	<dc:creator>Agung Premananda, I Gusti</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Exam timetabling problem; Great deluge; Hyper-heuristics; Simulated annealing; Tabu search</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23277</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i2.pp2155-2164</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 2: June 2024; 2155-2164</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23277/14019</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>