The effectiveness of sensor networks depends largely on the coverage provided by sensor deployment schemes. In order to improve the coverage rate of sensor network, it is necessary to optimize the sensor layout. In this paper, an optimized layout method of fiber Bragg grating (FBG) sensor network based on modified artificial fish swarm algorithm (MAFSA) is proposed. Firstly, the elliptical sensing model of FBG is established. According to the transfer characteristics of FBG, the exponential attenuation model between the sensor node and signal source is constructed. Secondly, taking the network coverage rate as the objective function, the movement of nodes is analogized to the behavior of artificial fish such as swarming, following and preying. In the process of status updating for artificial fish, aiming at the defects of the standard artificial fish swarm algorithm (AFSA) in optimization, three adaptive step methods are proposed. The convergence accuracy and speed of the algorithm are improved. Finally, the performance of the MAFSA is evaluated with particle swarm optimization algorithm and genetic algorithm. The results indicate that the MASFA has stronger search performance and can make the sensor node layout more reasonable, which is suitable for solving the problem of the sensor layout optimization.

传感器网络的有效性在很大程度上取决于传感器部署方案提供的覆盖范围。为了提高传感器网络的覆盖率，需要优化传感器布局。本文提出了一种基于改进人工鱼群算法(MAFSA)的光纤布拉格光栅(FBG)传感器网络优化布局方法。首先，建立了FBG的椭圆传感模型。根据FBG的传输特性，构建了传感器节点与信号源之间的指数衰减模型。其次，以网络覆盖率为目标函数，将节点的运动类比为人造鱼的蜂拥、跟随、捕食等行为。在人工鱼的状态更新过程中，针对标准人工鱼群算法(AFSA)在优化方面的缺陷，提出了三种自适应步长方法。提高了算法的收敛精度和速度。最后，用粒子群优化算法和遗传算法对MAFSA的性能进行了评估。结果表明，MASFA具有更强的搜索性能，可以使传感器节点布局更加合理，适合解决传感器布局优化问题。