基于改进粒子群优化算法的光纤传感网络的布置优化
Layout Optimization of Optical Fiber Sensor Network Based on Improved Particle Swarm Optimization Algorithm

光纤光栅传感网络的有效性在很大程度上取决于传感器部署方案所提供的覆盖范围。为了提高光纤光栅传感网络的覆盖率，需要对传感器的布置进行优化。本文提出一种基于改进粒子群算法的光纤传感网络的布置优化，通过余弦自适应调整惯性权重，同时利用惯性权重来调整学习因子，提高了算法的收敛精度。并基于传统传感器的检测区域模型，建立了更加贴合光纤光栅传感器特点的检测区域模型。以光纤光栅传感器网络覆盖率为目标函数，使用改进粒子群算法对传感器布置进行优化研究。仿真实验结果表明，在传感器节点数为25时，与PSO、UPSO、IABC三种智能优化算法相比，改进后的粒子群算法对目标区域的覆盖提升率分别提高了5.54%、3.81%、4.73%，在解决传感器节点覆盖优化问题方面表现出了显著的优越性。
The effectiveness of FBG sensor networks depends heavily on the coverage provided by sensor de-ployment scenarios. In order to improve the coverage of FBG sensor networks, the sensor placement needs to be optimized. In this paper, a layout optimization of optical fiber sensor network based on improved particle swarm algorithm is proposed, which adjusts the inertia weight by cosine adapta-tion, and uses the inertia weight to adjust the learning factor, which improves the convergence ac-curacy of the algorithm. Based on the detection area model of the traditional sensor, a detection ar-ea model that is more in line with the characteristics of FBG sensors is established. Taking the FBG sensor network coverage as the objective function, the improved particle swarm algorithm is used to optimize the sensor layout. The experimental results show that compared with the three intelli-gent optimization algorithms of PSO, UPSO and IABC, the coverage improvement rate of the im-proved particle swarm algorithm on the target area is increased by 5.54%, 3.81% and 4.73%, re-spectively, when the number of sensor nodes is 25, which shows significant superiority in solving the problem of sensor node coverage optimization.