TY - JOUR
T1 - Adaptive Learning Modified Great Deluge Hyper-Heuristics
AU - Hutama, Rizal Risnanda
AU - Muklason, Ahmad
N1 - Publisher Copyright:
© (2024), (Research Institute of Intelligent Computer Systems). All rights reserved.
PY - 2024
Y1 - 2024
N2 - The International Timetabling Competition (ITC) 2021 focuses on sports scheduling, a domain intricately connected to optimizing combinatorics problems. Within the framework of the ITC 2021 challenge, a crucial task is to precisely allocate matches to their designated time slots. Addressing this challenge involves the utilization of the Adaptive Learning Modified Great Deluge (ALMGD) algorithm, which belongs to the realm of hyper-heuristics. This algorithm represents an evolutionary step from the foundational great deluge algorithm, incorporating an acceptance mechanism intricately woven with self-adaptive learning. To assess its efficacy, the performance of the ALMGD algorithm is scrutinized through a comparative analysis with the hill climbing and great deluge algorithms. As a result, the proposed algorithm can produce a solution that is superior to the comparison algorithm. The modified great deluge algorithm can reduce the penalty by 36%, while the hill climbing algorithm can only reduce the penalty by 29% and the great deluge algorithm reaches 34%.
AB - The International Timetabling Competition (ITC) 2021 focuses on sports scheduling, a domain intricately connected to optimizing combinatorics problems. Within the framework of the ITC 2021 challenge, a crucial task is to precisely allocate matches to their designated time slots. Addressing this challenge involves the utilization of the Adaptive Learning Modified Great Deluge (ALMGD) algorithm, which belongs to the realm of hyper-heuristics. This algorithm represents an evolutionary step from the foundational great deluge algorithm, incorporating an acceptance mechanism intricately woven with self-adaptive learning. To assess its efficacy, the performance of the ALMGD algorithm is scrutinized through a comparative analysis with the hill climbing and great deluge algorithms. As a result, the proposed algorithm can produce a solution that is superior to the comparison algorithm. The modified great deluge algorithm can reduce the penalty by 36%, while the hill climbing algorithm can only reduce the penalty by 29% and the great deluge algorithm reaches 34%.
KW - Adaptive Learning Modified Great Deluge
KW - Combinatorial Optimization
KW - Hyper-Heuristics
KW - Sport Scheduling
UR - http://www.scopus.com/inward/record.url?scp=85198130527&partnerID=8YFLogxK
U2 - 10.47839/ijc.23.2.3549
DO - 10.47839/ijc.23.2.3549
M3 - Article
AN - SCOPUS:85198130527
SN - 1727-6209
VL - 23
SP - 287
EP - 293
JO - International Journal of Computing
JF - International Journal of Computing
IS - 2
ER -