An Inclusive Survey on Marine Predators Algorithm: Variants and Applications.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Marine Predators Algorithm (MPA) is a population-based meta-heuristic optimization algorithm introduced in 2020.
• It is inspired by the foraging strategies of marine predators, utilizing Levy or Brownian motion and optimal encounter rates.
• MPA has rapidly gained popularity due to its simplicity, effectiveness, and ability to solve various optimization problems.

Purpose of the Study:

• To provide a comprehensive review of the Marine Predators Algorithm (MPA).
• To summarize MPA variants and their characteristics.
• To illustrate the wide-ranging applications of MPA across different scientific and engineering domains.

Main Methods:

• The paper summarizes the core principles and mathematical formulations of the Marine Predators Algorithm.
• It reviews documented variants of MPA, highlighting modifications and improvements.
• A systematic survey of MPA's application in diverse fields is presented.

Main Results:

• MPA demonstrates superior performance in convergence speed and effectiveness compared to other algorithms.
• It is capable of solving continuous, multi-objective, and binary optimization problems.
• MPA has been successfully applied in areas including image processing, power systems, and task scheduling.

Conclusions:

• The Marine Predators Algorithm is a versatile and effective optimization tool with significant potential.
• Its adaptability makes it suitable for a broad spectrum of complex problems.
• Future research directions for MPA are identified, suggesting avenues for further development and application.