Marine Predator Algorithm (MPA) is a metaheuristic algorithm inspired by the hunting behavior of marine predators, such as sharks, whales, and dolphins. MPA is used to solve optimization problems, particularly in engineering and computer science. In MPA, the search process is carried out by three types of agents: predators, prey, and obstacles.
Predators represent the best solutions found so far and search for new solutions by following the movements of the prey. The prey represents the potential solutions and moves randomly in the search space. Obstacles represent the regions of the search space that the predators and prey should avoid.
During the search process, the predators use a set of rules to adjust their position and search direction. These rules include swimming toward the prey, avoiding obstacles, and adjusting the swimming speed based on the distance to the prey. The prey also uses a set of rules to adjust its position and movement, such as randomly swimming in the search space and avoiding predators.
Marine Predator Algorithm has several advantages:
• Simplicity
• Flexibility
• Ability to handle nonlinear
• Non-convex and
• Multimodal optimization problems.
It can also efficiently converge to the global optima with a small number of function evaluations. MPA has been applied to various fields, such as structural optimization, image processing, and machine learning. It is a promising approach for solving complex optimization problems, especially when other methods fail to converge.