Solving course timetabling problem using Whale Optimization Algorithm

Austero, Lea D.; Sison, Ariel M.; Matias, Junrie B.; Medina, Ruji P.

doi:10.1109/itt56123.2022.9863951

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…After numerous successful deployments of updated and hybridized FA variants, there are still opportunities for improvement. Initiatives to hybridize metaheuristic algorithms, such as those described in [25]- [27] have emerged. Similarly, the exploration mechanism of the FA can be combined with processes from various metaheuristics.…”

Section: Metaheuristicmentioning

confidence: 99%

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Villaruz¹,

Gerardo²,

Gamao³

et al. 2023

IJACSA

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For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, the standard firefly algorithm (FA) posted some limitations in its exploration process that can eventually lead to premature convergence, affecting its performance and adding uncertainty to the optimization results. To address these constraints, this study introduces an additional novel search mechanism for the standard FA inspired by the behavior of the scout bee in the artificial bee colony (ABC) algorithm, termed the "Scouting FA". Specifically, fireflies stuck in the local optima will take directed extra random walks to escape toward the region of the optimum solution, thus improving convergence accuracy. Empirical findings on the five standard benchmark functions have validated the effects of this modification and revealed that Scouting FA is superior to its original version.

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Section: Metaheuristicmentioning

confidence: 99%

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Villaruz¹,

Gerardo²,

Gamao³

et al. 2023

IJACSA

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“…WOA, like most metaheuristic algorithms, struggles to balance local and global searches. The present study is an application and enhancement of the algorithm used in the previous work of WOA [14] in solving course timetabling. In this work, WOA was integrated with heuristic mutation.…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

“…This section presents the problem description and objective functions to be used in the implementation of the timetable. This study's constraints and objective functions are similar to the previous work [14] but were implemented with a different algorithm to enhance WOA.…”

Section: A Problem Definitionmentioning

confidence: 99%

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Optimizing Faculty Workloads and Room Utilization using Heuristically Enhanced WOA

Austero¹,

Sison²,

Matias³

et al. 2022

IJACSA

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The creation and generation of schedules that are free of conflicts manually every academic semester present higher education institutions with a duty that is laborious and demanding of their resources. The course timetabling optimization, as an education timetabling problem, is a popular example of an NP-hard combinatorial problem. Numerous attempts have been made over the course of the past few decades to find a solution to this problem, but no one has yet developed a foolproof approach that can examine all alternatives to find the best method. The promising swarm-based optimization algorithm called Whale Optimization Algorithm was heuristically enhanced in the present study and is called HEWOA. It was designed as a solution to the course timetabling problem discussed in the current study. HEWOA was able to generate an efficient timetable for the large dataset of 1700 events for an average time of 14.92 seconds only, with an average generation of 7.2 and a best time of 8.38 seconds. These results reveal that the performance of HEWOA was better than that of various hybrids of the Genetic Algorithm that was compared in the present study.

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Modified and hybridised bi-objective firefly algorithms for university course scheduling

Thepphakorn

Pongcharoen

2023

Soft Comput

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Solving course timetabling problem using Whale Optimization Algorithm

Cited by 4 publications

References 28 publications

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Optimizing Faculty Workloads and Room Utilization using Heuristically Enhanced WOA

Modified and hybridised bi-objective firefly algorithms for university course scheduling

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