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Updated: Nov 1, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Published on: March 20, 2017

Dynamic mutation enhanced particle swarm optimization for optical wavefront shaping.

Chi Man Woo, Huanhao Li, Qi Zhao

|June 22, 2021

Summary

This summary is machine-generated.

Particle swarm optimization (PSO) with dynamic mutation improves light focusing in scattering media. This enhanced algorithm overcomes premature convergence, achieving near-optimal results even in noisy conditions.

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Area of Science:

Optics and Photonics
Computational Physics
Image Processing

Background:

Particle Swarm Optimization (PSO) is widely used for wavefront shaping to focus light through scattering media.
A key limitation of standard PSO is its tendency towards premature convergence, especially in unstable or noisy environments.
This hinders optimal light focusing performance in complex optical systems.

Purpose of the Study:

To enhance the light focusing performance of Particle Swarm Optimization (PSO) in scattering media.
To address the issue of premature convergence in unstable environments by introducing a dynamic mutation strategy.
To improve the adaptability and robustness of PSO algorithms for optical wavefront shaping.

Main Methods:

A dynamic mutation operation was integrated into the standard Particle Swarm Optimization (PSO) algorithm.
The mutation rate was dynamically adjusted based on the quantifiable discrepancy (error rate) between the current and theoretical optimal solution.
Simulations and experimental validations were conducted to evaluate the performance of the enhanced PSO algorithm.

Main Results:

The dynamic mutation strategy effectively expanded particle diversity, enhancing algorithm adaptability to noise and instability.
The modified PSO algorithm demonstrated optimization performance approaching the theoretical optimum.
Compared to standard PSO or PSO with constant mutation, the dynamic mutation approach showed significantly better focusing performance, particularly in noisy environments.

Conclusions:

Particle Swarm Optimization with dynamic mutation offers a superior approach for wavefront shaping and light focusing in scattering media.
The dynamic mutation mechanism effectively mitigates premature convergence and improves robustness against environmental instability and noise.
This enhanced algorithm holds significant potential for applications requiring precise light control in challenging optical conditions.