Marine Predators Algorithm: A nature-inspired metaheuristic

Afshin Faramarzi, Mohammad Heidarinejad, Seyedali Mirjalili, Amir H. Gandomi

Research output: Contribution to journalArticlepeer-review

1354 Citations (Scopus)


This paper presents a nature-inspired metaheuristic called Marine Predators Algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Lévy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems. This paper evaluates the MPA's performance on twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape, three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy performance. MPA is compared with three classes of existing optimization methods, including (1) GA and PSO as the most well-studied metaheuristics, (2) GSA, CS and SSA as almost recently developed algorithms and (3) CMA-ES, SHADE and LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-cnEpSin. The source code is publicly available at:,,, and

Original languageEnglish
Article number113377
JournalExpert Systems with Applications
Publication statusPublished - 15 Aug 2020


  • Evolutionary computation
  • Global optimization
  • Marine Predators Algorithm
  • Metaheuristic
  • Stochastic optimization
  • Swarm intelligence


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