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Related Experiment Video

Updated: Jun 27, 2025

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

An improved manta ray foraging optimization algorithm.

Pengju Qu^1,2, Qingni Yuan³, Feilong Du¹

¹Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China.

Scientific Reports

|May 5, 2024

Summary

This summary is machine-generated.

The Improved Manta Ray Foraging Optimization Algorithm (IMRFO) enhances convergence and avoids local optima using chaotic mapping and Levy flight. This improved algorithm demonstrates superior performance in optimization tasks.

Keywords:

Bidirectional search strategy Improved manta ray foraging optimization Levy flight Metaheuristic

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristics

Background:

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic approach for complex problem-solving.
Existing MRFO methods face challenges with slow convergence and local optima entrapment.

Purpose of the Study:

To introduce an Improved Manta Ray Foraging Optimization Algorithm (IMRFO).
To enhance the convergence speed and global search capability of the MRFO algorithm.

Main Methods:

Incorporation of Tent chaotic mapping for improved initial solution quality and uniform distribution.
Implementation of a bidirectional search strategy to broaden the exploration range.
Integration of Levy flight strategy to enhance the ability to escape local optima.

Main Results:

IMRFO was rigorously tested against 10 other algorithms on 23 benchmark functions and CEC2017/2022 suites.
Statistical analysis confirmed the significant superiority of IMRFO over competing optimization algorithms.
The proposed IMRFO algorithm demonstrated superior performance on five engineering problems.

Conclusions:

The IMRFO algorithm effectively overcomes the limitations of the standard MRFO.
The enhanced strategies significantly improve optimization performance and solution quality.
IMRFO represents a robust and effective advancement in metaheuristic optimization.