Marine Predator Algorithm and Related Variants: A Systematic Review

Emmanuel, Philibus and Azlan, Mohd Zain and Didik Dwi, Prasetya and Mahadi, Bahari and Norfadzlan, Yusup and Rozita, Abdul Jalil and Mazlina, Abdul Majid and Azurah, A Samah (2025) Marine Predator Algorithm and Related Variants: A Systematic Review. International Journal of Advanced Computer Science and Applications (IJACSA), 16 (1). pp. 544-568. ISSN 2156-5570, 2158-107X

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Abstract

—The Marine Predators Algorithm (MPA) is classified under swarm intelligence methods based on its type of inspiration. It is a population-based metaheuristic optimization algorithm inspired by the general foraging behavior exhibited in the form of Levy and Brownian motion in ocean predators supported by the policy of optimum success rate found in the biological relationship between prey and predators. The algorithm is easy to implement and robust in searching, yielding better solutions to many real-world problems. It is attracting huge and growing interest. This paper provides a systematic review of the research progress and applications of the MPA by analyzing more than 100 articles sourced from Scopus and Web of Science databases using the PRISMA approach. The study expounded the classical MPA’s workflow. It also unveiled a steady upward trend in the use of the algorithm. The research presented different improvements and variants of MPA including parameter-tuning, enhancement of the balance between exploration and exploitation, hybridization of MPA with other techniques to harness the strengths of each of the algorithms towards complementing thecweaknesses of the other, and more recently proposed advances. It further underscores the application of MPA in various areas such as Engineering, Computer Science, Mathematics, and Energy. Findings reveal several search strategies implemented to improve the algorithm’s performance. In conclusion, although MPA has been widely accepted, other areas remain yet to be applied, and some improvements are yet to be covered. These have been presented as recommendations for future research direction.

Item Type: Article
Uncontrolled Keywords: Exploitation-exploration; marine predator algorithm; metaheuristic algorithms; metaheuristic-hybridization; meta-heuristics; optimization; predator prey systems.
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Faculties, Institutes, Centres > Faculty of Computer Science and Information Technology
Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Yusup
Date Deposited: 05 Feb 2025 01:53
Last Modified: 05 Feb 2025 01:53
URI: http://ir.unimas.my/id/eprint/47488

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