Marine Predator Algorithm and Related Variants: A Systematic Review
—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 support...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SCIENCE & INFORMATION SAI ORGANIZATION LTD
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/47488/1/Paper_54-Marine_Predator_Algorithm_and_Related_Variants.pdf http://ir.unimas.my/id/eprint/47488/ https://thesai.org/Downloads/Volume16No1/Paper_54-Marine_Predator_Algorithm_and_Related_Variants.pdf http://dx.doi.org/10.14569/IJACSA.2025.0160154 |
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| Summary: | —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. |
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