Improved Adaptive Multi-Objective Particle Swarm Optimization of Sensor Layout for Shape Sensing with Inverse Finite Element Method

Abstract

The inverse finite element method (iFEM) is one of the most effective deformation reconstruction techniques for shape sensing, which is widely applied in structural health monitoring. The distribution of strain sensors affects the reconstruction accuracy of the structure in iFEM. This paper proposes a method to optimize the layout of sensors rationally. Firstly, this paper constructs a dual-objective model based on the accuracy and robustness indexes. Then, an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm is developed for this model, which introduces initialization strategy, the adaptive inertia weight strategy, the guided particle selection strategy and the external candidate solution (ECS) set maintenance strategy to multi-objective particle swarm optimization (MOPSO). Afterwards, the performance of IAMOPSO is verified by comparing with MOPSO applied on the existing inverse beam model. Finally, the IAMOPSO is employed to the deformation reconstruction of complex plate-beam model. The numerical and experimental results demonstrate that the IAMOPSO is an excellent tool for sensor layout in iFEM.

Coarse and Fine Two-Stage Calibration Method for Enhancing the Accuracy of Inverse Finite Element Method

“…The parameterization of the data points reflects the nature of the curve constructed with the data points. Based on the nodal vector of basis functions and control points to construct the NURBS fitted curves, a centripetal parameterization method is applied to the sample data as shown in Equation (30).…”

“…To address the problem of the effect of a small sample size on reconstruction accuracy, Xu et al [29] proposed a two-step calibration method, but this method is limited to the reconstruction displacement accuracy at the maximum deformation position, and the reconstruction accuracy at the rest of the positions is not as accurate. Li et al [30] determined the layout location of sensors based on a multi-objective particle swarm optimization algorithm. The fuzzy network calibration method for the small sample problem was proposed by Li et al [31], which improves the reconstructed accuracy of the whole displacement field effectively.…”

Hybrid Shell-Beam Inverse Finite Element Method for the Shape Sensing of Stiffened Thin-Walled Structures: Formulation and Experimental Validation on a Composite Wing-Shaped Panel

“…Numerical and experimental application of the 1D iFEM feature the monitoring of circular and airfoil beams [54,57], radio telescope reflectors [58], wing structures [59], subsea pipelines [60], etc. Recent efforts have also been aimed at non-linear deformation monitoring [61] and optimal sensor placement [46,47,62] for efficient shape sensing.…”

A Large-Scale Sensor Layout Optimization Algorithm for Improving the Accuracy of Inverse Finite Element Method

“…In [20], the optimization was performed using a multi-objective particle swarm algorithm (MOPSO) with robustness and accuracy as optimization objectives, but the absence of an appropriate strategy to ensure diversity resulted in a tendency to get trapped in the local optima. By introducing strategies such as guided particle selection and maintenance of an external candidate solution set, Li et al improved the optimization performance [21]. The above algorithms improve the optimization by introducing different methods, but the effectiveness of these methods significantly diminishes when the dimensionality of the solution space becomes too large.…”

“…These groupings are usually simpler, but since they do not take into account the attributes of the decision variables, their enhancement effect on optimization is small. The optimizer is the algorithm used to optimize each subgroup, such as the aforementioned MOPSO algorithm [20,21]; they perform poorly when faced with high-dimensional optimization problems. The collaboration method defines the inter-group collaboration strategy, which specifies the content and manner of information sharing between subgroups [27].…”