Emmanuel Philibus, Azlan Mohd Zain, Didik Dwi Prasetya, Mahadi Bahari, Norfadzlan bin Yusup, Rozita Abdul Jalil, Mazlina Abdul Majid, Azurah A. Samah
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 the weaknesses 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. © (2025), (Science and Information Organization). All rights reserved.
Department of Computer Science, Universiti Teknologi Malaysia, Johor Bahru, Malaysia; Department of Computer Science, Kaduna State College of Education, Kafanchan, Gidan Waya, Nigeria; Department of Electrical Engineering and Informatics, State University of Malang, Malang, Indonesia; Department of Information Systems, Universiti Teknologi Malaysia, Johor Bahru, Malaysia; Department of Software Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia; Department of Software Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia; Centre for Artificial Intelligence & Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, Malaysia
