Zhao Zhenyi, Chen Kangyu, Liu Yimin, Bao Hong
Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China.
Intelligent Robot Laboratory, Hangzhou Research Institute of Xidian University, Hangzhou 311231, China.
Sensors (Basel). 2023 Sep 29;23(19):8176. doi: 10.3390/s23198176.
The inverse finite element method (iFEM) based on fiber grating sensors has been demonstrated as a shape sensing method for health monitoring of large and complex engineering structures. However, the existing optimization algorithms cause the local optima and low computational efficiency for high-dimensional strain sensor layout optimization problems of complex antenna truss models. This paper proposes the improved adaptive large-scale cooperative coevolution (IALSCC) algorithm to obtain the strain sensors deployment on iFEM, and the method includes the initialization strategy, adaptive region partitioning strategy, and selection and particle updating strategies, enhancing the reconstruction accuracy of iFEM for antenna truss structure and algorithm efficiency. The strain sensors optimization deployment on the antenna truss model for different postures is achieved, and the numerical results show that the optimization algorithm IALSCC proposed in this paper can well handle the high-dimensional sensor layout optimization problem.
基于光纤光栅传感器的逆有限元方法（iFEM）已被证明是一种用于大型复杂工程结构健康监测的形状传感方法。然而，现有的优化算法在复杂天线桁架模型的高维应变传感器布局优化问题上会导致局部最优和计算效率低下。本文提出了改进的自适应大规模协同进化（IALSCC）算法，以获得iFEM上的应变传感器部署，该方法包括初始化策略、自适应区域划分策略以及选择和粒子更新策略，提高了iFEM对天线桁架结构的重建精度和算法效率。实现了天线桁架模型在不同姿态下的应变传感器优化部署，数值结果表明本文提出的优化算法IALSCC能够很好地处理高维传感器布局优化问题。
