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Article information
2020 , Volume 25, ¹ 4, p.20-30
Khudozhitkova D.A.
Optimization of a mode-locked fiber laser based on nonlinear polarization rotation using genetic algorithm
Purpose. The article addresses optimization of mode-locked fiber laser based on nonlinear polarization rotation. Determination of the resonator parameters corresponding to the single-pulse with the highest energy has been performed using the genetic algorithm. Metodology. Mathematical modelling has been carried out with the help of two models, namely, scalar and hybrid. Scalar model describes signal propagation by generalized nonlinear Schr¨odinger equation. Hybrid model describes section of DCF by pair of generalized nonlinear Schr¨odinger equations. Numerical simulation has been performed by the split-step Fourier method. Genetic algorithm has been implemented as SteadyState method by GALib library. The value of the fitness function is equal to the energy value for the single-pulse and otherwise zero. Findings. The genetic algorithm has been implemented for solving optimization problem of fiber lasers and modifying the algorithm to reduce the calculation time. The parameters of the laser for the scalar model are determined and agreement with the results obtained by the enumeration method is presented. For the hybrid model, a stable pulse with three times the energy was found. Conclusions. In this work, a determination of the optimal parameters for mode-locked fiber lasers based on nonlinear polarization rotation was presented. The problem was solved using a genetic algorithm. This is the first step towards creating a “smart” self-tuning laser
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Keywords: mathematical modelling, fiber lasers, optimization, genetic algorithm
doi: 10.25743/ICT.2020.25.4.003
Author(s):
Khudozhitkova Dar'ya Alekseevna
Position: Student
Office: Novosibirsk State University
Address: 630090, Russia, Novosibirsk, 2, Pirogova str.
E-mail: khudozhitkova.da@gmail.com
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Bibliography link:
Khudozhitkova D.A. Optimization of a mode-locked fiber laser based on nonlinear polarization rotation using genetic algorithm // Computational technologies. 2020. V. 25. ¹ 4. P. 20-30
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