Evolutionary Harmony Search Algorithm for Sport Scheduling Problem

Khelifa, Meriem; Boughaci, Dalila; Aïmeur, Esma

doi:10.1007/978-3-319-99810-7_5

Cited by 1 publication

(1 citation statement)

References 41 publications

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“…Examples of sports that have been conducted in scheduling research are football [6,7], table tennis [8], basketball [9][10][11], and so on. Algorithms that have been used to solve sports scheduling optimization are adaptive large neighborhood search [5], a combination of greedy randomized adaptive search and iterated local search [12], harmony search [13] and so on. Sports scheduling often appears with a round robin model [14].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning Modified Great Deluge Hyper-Heuristics

Hutama,

Muklason

2024

IJC

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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%.

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