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Fusion: Practice and Applications
Volume 13 , Issue 1, PP: 89-102 , 2023 | Cite this article as | XML | Html |PDF

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A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications

  Muhammad A. S. Mohd Shahrom 1 * ,   Nurezayana Zainal 2 ,   Mohamad F. Ab. Aziz 3 ,   Salama A. Mostafa 4

1  Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
    (hi210017@student.uthm.edu.my)

2  Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
    (nurezayana@uthm.edu.my)

3  Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
    (mdfirdaus@uthm.edu.my)

4  Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
    (salama@uthm.edu.my)

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Doi   :   https://doi.org/10.54216/FPA.130107

Received: March 18, 2023 Revised: June 24, 2023 Accepted: August 27, 2023

Abstract :

Natural phenomena inspire the meta-heuristic algorithm to carry out the aim of reaching the optimal solution. Glowworm swarm optimization (GSO) is an original swarm intelligence algorithm for optimization, which mimic the glow behavior of glowworm that can effectively capture the maximum multimodal function. GSO is part of the meta-heuristic algorithm used to solve the optimization problem. This algorithm solves many problems in optimization, especially in science, engineering, and network. Therefore, this paper review exposes the GSO method in solving the problem in any industry area. This study focuses on the basic flow of GSO, the modification of GSO, and the hybridization of GSO by conducting the previous study of the researcher. Based on this study, the GSO application in the engineering industry gets the highest score of 15% among other sectors.

Keywords :

Meta-heuristic; swarm intelligence; glowworm swarm optimization; multimodal function; optimization.

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Cite this Article as :
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MLA Muhammad A. S. Mohd Shahrom, Nurezayana Zainal , Mohamad F. Ab. Aziz, Salama A. Mostafa. "A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications." Fusion: Practice and Applications, Vol. 13, No. 1, 2023 ,PP. 89-102 (Doi   :  https://doi.org/10.54216/FPA.130107)
APA Muhammad A. S. Mohd Shahrom, Nurezayana Zainal , Mohamad F. Ab. Aziz, Salama A. Mostafa. (2023). A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications. Journal of Fusion: Practice and Applications, 13 ( 1 ), 89-102 (Doi   :  https://doi.org/10.54216/FPA.130107)
Chicago Muhammad A. S. Mohd Shahrom, Nurezayana Zainal , Mohamad F. Ab. Aziz, Salama A. Mostafa. "A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications." Journal of Fusion: Practice and Applications, 13 no. 1 (2023): 89-102 (Doi   :  https://doi.org/10.54216/FPA.130107)
Harvard Muhammad A. S. Mohd Shahrom, Nurezayana Zainal , Mohamad F. Ab. Aziz, Salama A. Mostafa. (2023). A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications. Journal of Fusion: Practice and Applications, 13 ( 1 ), 89-102 (Doi   :  https://doi.org/10.54216/FPA.130107)
Vancouver Muhammad A. S. Mohd Shahrom, Nurezayana Zainal , Mohamad F. Ab. Aziz, Salama A. Mostafa. A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications. Journal of Fusion: Practice and Applications, (2023); 13 ( 1 ): 89-102 (Doi   :  https://doi.org/10.54216/FPA.130107)
IEEE Muhammad A. S. Mohd Shahrom, Nurezayana Zainal, Mohamad F. Ab. Aziz, Salama A. Mostafa, A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications, Journal of Fusion: Practice and Applications, Vol. 13 , No. 1 , (2023) : 89-102 (Doi   :  https://doi.org/10.54216/FPA.130107)
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