Implementation of the Marine Predators Algorithm (MPA) in R. MPA is a nature-inspired metaheuristic optimization algorithm that follows the rules governing optimal foraging strategy and encounter rate policy between predator and prey in marine ecosystems.
Main optimization function implementing the MPA algorithm
Retrieve benchmark function parameters
Generate Levy flight random steps
Initialize search agent population
The package includes 23 standard benchmark functions (F1-F23) for testing
optimization algorithms. See test-functions for a complete list.
library(marinepredator)# Basic optimization with the Sphere function result <- mpa( SearchAgents_no = 30, Max_iter = 100, lb = -100, ub = 100, dim = 30, fobj = F01 ) print(result)
Maintainer: Marc Grossouvre marcgrossouvre@urbs.fr (R implementation) [translator]
Authors:
Afshin Faramarzi (Original MATLAB code)
Seyedali Mirjalili (Original MATLAB code)
Other contributors:
URBS company contact@rubs.fr [copyright holder, funder]
The Marine Predators Algorithm is based on the foraging behavior of marine predators and their interactions with prey. The algorithm operates in three phases that balance exploration and exploitation:
Phase 1 (High velocity ratio): Prey moves faster than predator. Exploration is emphasized using Brownian motion.
Phase 2 (Unit velocity ratio): Predator and prey move at similar speeds. Mixed strategy with both Brownian motion and Levy flight.
Phase 3 (Low velocity ratio): Predator moves faster than prey. Exploitation is emphasized using Levy flight.
Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A Nature-inspired Metaheuristic. Expert Systems with Applications, 152, 113377. tools:::Rd_expr_doi("10.1016/j.eswa.2020.113377")
Useful links:
GitHub repository: https://github.com/urbs-dev/marinepredator
Report bugs: https://github.com/urbs-dev/marinepredator/issues