基于Pareto多目标优化的光纤Bragg光栅传感网络的波长分配

Wavelength assignment of FBG sensor network based on Pareto multi-objective optimization

针对现有波分复用(WDM)的光纤Bragg光栅(FBG)传感网络的复用瓶颈,运用Pareto多目标优化理论,建立了基于带宽重叠技术的FBG传感网络优化模型。通过非支配排序遗传算法Ⅱ(NSGA-Ⅱ)进化算法求解Pareto最优曲线,为网络中的每个FBG传感器合理地分配Bragg波长的工作范围,以最小的光谱重叠程度换取光源带宽资源的最大节约。仿真和实验结果表明,得到Pareto最优曲线为不同程度的光谱重叠找到了最优的Bragg波长配置方案,有效地提高了FBG传感网络的WDM能力。

There is a bottleneck in the wavelength division multiplexed （WDM） fiber Bragg grating （FBG） sensor network. The conventional WDM technique requires that each FBG sensor in the array must occupy a unique spectral region. It seriously limits the maximum number or the measurement range of sensors in the network. In order to improve the performance of a WDM FBG sensor network, the Pareto-based multi-objective optimization technology is introduced to design an optimal wavelength assignment in this paper. The two objectives are used to minimize the spectrum overlapping area and the band- width of the optical source. In this multi-objective model, there are a set of acceptable trade-off optimal soulutions, called Pareto front. The non-dominated sorting genetic algorithm II （NSGA-II）,which is one of the most efficient and famous multi-objective evolutionary algorithms,is applied for obtaining the Pareto front. The achieved Pareto from can help the decision maker to select a suitable solution. Simulation reslts show that the Pareto optimzal solutions provide good wavelength assignments for various ovelapping spectra and the proposed approach is effective in saving the source bandwidth and increasing the number of FBG sensors in the WDM FBG sensor network.