Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study
Abstract Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report...
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Nature Portfolio
2025-01-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-024-84567-x |
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author | Mathilde Hary Teemu Koivisto Sara Lukasik John M. Dudley Goëry Genty |
author_facet | Mathilde Hary Teemu Koivisto Sara Lukasik John M. Dudley Goëry Genty |
author_sort | Mathilde Hary |
collection | DOAJ |
description | Abstract Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase of the input pulses, different optimization approaches including a genetic algorithm, particle swarm optimizer, and simulated annealing. We find that the genetic algorithm and particle swarm optimizer are more robust and perform better, with the particle swarm optimizer converging faster. Our study provides valuable insights for the systematic optimization of supercontinuum and other optical sources. |
format | Article |
id | doaj-art-80231823f59848488f4347a4890ed845 |
institution | Kabale University |
issn | 2045-2322 |
language | English |
publishDate | 2025-01-01 |
publisher | Nature Portfolio |
record_format | Article |
series | Scientific Reports |
spelling | doaj-art-80231823f59848488f4347a4890ed8452025-01-05T12:14:55ZengNature PortfolioScientific Reports2045-23222025-01-011511810.1038/s41598-024-84567-xSpectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative studyMathilde Hary0Teemu Koivisto1Sara Lukasik2John M. Dudley3Goëry Genty4Photonics Laboratory, Tampere UniversityPhotonics Laboratory, Tampere UniversityFaculty of Physics, University of WarsawInstitut FEMTO-ST, Université Bourgogne Franche-Comté CNRS UMR 6174Photonics Laboratory, Tampere UniversityAbstract Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase of the input pulses, different optimization approaches including a genetic algorithm, particle swarm optimizer, and simulated annealing. We find that the genetic algorithm and particle swarm optimizer are more robust and perform better, with the particle swarm optimizer converging faster. Our study provides valuable insights for the systematic optimization of supercontinuum and other optical sources.https://doi.org/10.1038/s41598-024-84567-x |
spellingShingle | Mathilde Hary Teemu Koivisto Sara Lukasik John M. Dudley Goëry Genty Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study Scientific Reports |
title | Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study |
title_full | Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study |
title_fullStr | Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study |
title_full_unstemmed | Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study |
title_short | Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study |
title_sort | spectral optimization of supercontinuum shaping using metaheuristic algorithms a comparative study |
url | https://doi.org/10.1038/s41598-024-84567-x |
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