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

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.