Open AccessMultimode fiber modal decomposition based on hybrid genetic global optimization algorithm
Author(s) -
Lei Li,
Jinyong Leng,
Pu Zhang,
Jinbao Chen
Publication year - 2017
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.25.019680
Subject(s) - maxima and minima , genetic algorithm , algorithm , computer science , convergence (economics) , fiber laser , modal , stochastic gradient descent , gradient descent , global optimization , broyden–fletcher–goldfarb–shanno algorithm , optics , optical fiber , mathematical optimization , mathematics , physics , materials science , artificial neural network , artificial intelligence , mathematical analysis , computer network , asynchronous communication , polymer chemistry , economics , economic growth , telecommunications
Numerical modal decomposition (MD) is an effective approach to reveal modal characteristics in high power fiber lasers. The main challenge is to find a suitable multi-dimensional optimization algorithm to reveal exact superposition of eigenmodes, especially for multimode fiber. A novel hybrid genetic global optimization algorithm, named GA-SPGD, which combines the advantages of genetic algorithm (GA) and stochastic parallel gradient descent (SPGD) algorithm, is firstly proposed to reduce local minima possibilities caused by sensitivity to initial values. Firstly, GA is applied to search the rough global optimization position based on near- and far-field intensity distribution with high accuracy. Upon those initial values, SPGD algorithm is afterwards used to find the exact optimization values based on near-field intensity distribution with fast convergence speed. Numerical simulations validate the feasibility and reliability.