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Related Experiment Video

Updated: Dec 14, 2025

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

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Published on: July 29, 2013

Design optimization of orbital angular momentum fibers using the gray wolf optimizer.

Seyed Mohammad Mirjalili, Hussein Taleb, M Z Kabir

|July 17, 2020

Summary

This study introduces an AI-driven framework for optimizing optical fiber designs for orbital angular momentum (OAM) communication. The AI framework efficiently identifies optimal fiber structures, significantly enhancing transmission capacity.

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Last Updated: Dec 14, 2025

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Area of Science:

Optical Communications Engineering
Materials Science
Artificial Intelligence

Background:

Orbital Angular Momentum (OAM) of light offers a novel approach to boost optical fiber transmission capacity.
Current OAM fiber designs, typically featuring ring-shaped refractive index profiles, face challenges in optimizing multiple performance metrics simultaneously.
The complex relationship between structural parameters and optical properties hinders traditional analytical design methods for OAM fibers.

Purpose of the Study:

To develop a comprehensive framework for designing optimized OAM fiber cross-section refractive index profiles.
To maximize key performance indicators: number of supported OAM modes, mode purity, and effective refractive index separation.
To address the multiobjective nature of OAM fiber design, seeking a range of optimal solutions with inherent trade-offs.

Main Methods:

Implementation of an artificial intelligence (AI) optimization framework.
Utilizing the multiobjective Gray Wolf Optimizer (GWO) algorithm to explore the design space.
Comparison of multiobjective GWO performance against single-objective GWO for OAM fiber design.

Main Results:

The AI framework successfully identified numerous optimal OAM fiber designs.
The optimized designs support a high number of OAM modes, exceeding 20 channels.
The proposed method demonstrates comprehensiveness and applicability to various OAM fiber structures.

Conclusions:

The AI-driven framework provides a powerful, automated solution for OAM fiber design.
The method simplifies the optimization process, requiring no human intervention.
This approach effectively balances competing design objectives, yielding a diverse set of high-performance OAM fiber solutions.