Marine Predators Algorithm (MPA)

Marine Predators Algorithm (MPA) is a nature-inspired optimization algorithm that follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems.

MPA’s performance is evaluated against 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.

Paper: A. Faramarzi, M. Heidarinejad, S. Mirjalili, A H Gandomi: Marine Predators Algorithm: A Nature-inspired Metaheuristic, Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113377

The source code of EO is also available at GitHub: https://github.com/afshinfaramarzi/Marine-Predators-Algorithm

If you don’t have access to the paper, just leave me a message at afaramar@hawk.iit.edu or afshin.faramarzi@gmail.com and I will get back to you soon.