Layout optimization of fiber Bragg grating strain sensor network based on modified artificial fish swarm algorithm

Abstract

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.