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基于混合遗传全局优化算法的多模光纤模态分解

Multimode fiber modal decomposition based on hybrid genetic global optimization algorithm.

作者信息

Li Lei, Leng Jinyong, Zhou Pu, Chen Jinbao

出版信息

Opt Express. 2017 Aug 21;25(17):19680-19690. doi: 10.1364/OE.25.019680.

Abstract

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.

摘要

数值模态分解(MD)是揭示高功率光纤激光器模态特性的一种有效方法。主要挑战在于找到一种合适的多维优化算法来揭示本征模的精确叠加,特别是对于多模光纤。首先提出了一种新颖的混合遗传全局优化算法,称为GA-SPGD,它结合了遗传算法(GA)和随机并行梯度下降(SPGD)算法的优点,以减少因对初始值敏感而导致的局部极小值可能性。首先,基于高精度的近场和远场强度分布,应用遗传算法搜索粗略的全局优化位置。基于这些初始值,随后使用SPGD算法基于近场强度分布以快速收敛速度找到精确的优化值。数值模拟验证了其可行性和可靠性。

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