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Predator-Prey Interactions

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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.

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In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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Published on: November 20, 2017

Marine Predators Algorithm: A Review.

Mohammed Azmi Al-Betar^1,2, Mohammed A Awadallah^3,4, Sharif Naser Makhadmeh¹

¹Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.

Archives of Computational Methods in Engineering : State of the Art Reviews

|June 1, 2023

Summary

This summary is machine-generated.

The Marine Predators Algorithm (MPA), inspired by ocean predator foraging, is a powerful, simple optimization tool. This review analyzes 102 studies, highlighting MPA

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Area of Science:

Computational Intelligence
Optimization Algorithms
Nature-Inspired Computing

Background:

The Marine Predators Algorithm (MPA) is a novel nature-inspired metaheuristic optimization algorithm.
It is based on the foraging strategies of ocean predators, incorporating Lévy and Brownian motion.
MPA's simplicity, parameter-less nature, and flexibility have led to its rapid adoption.

Purpose of the Study:

To comprehensively review and analyze the primary research studies on the Marine Predators Algorithm (MPA).
To illustrate the inspirations, theoretical underpinnings, and convergence behavior of MPA.
To examine improved MPA variants and their applications in diverse optimization problems.

Main Methods:

Systematic literature review of 102 research papers focusing on MPA.
Analysis of MPA's core concepts, including its foraging mechanisms and mathematical formulations.
Categorization and discussion of various MPA applications and enhanced versions.

Main Results:

MPA demonstrates powerful performance across a wide range of optimization problems.
Analysis of 102 studies confirms the algorithm's significant growth and effectiveness.
Various MPA versions have been developed to enhance its performance on complex, real-world problems.

Conclusions:

The review highlights MPA's impressive capabilities and its potential for future development.
Critical discussion identifies limitations and areas for improvement in MPA's convergence behavior.
Future research directions and potential extensions for MPA are proposed.