## 1 Introduction
In the optimization of measurement points, a finite element model of the structure needs to be established by finite element analysis software, and then the node data is extracted as the training data of the optimization algorithm; hence, how to effectively extract a large number of node data is vital for measurement point optimization. [PERSON] et al. [13] studied the optimal arrangement of bridge sensors based on the single parent genetic algorithm. Taking Guiliuhe Arch Bridge as an example, they used the APDL module of ANSYS to divide the bridge into 1440 units and 1276 nodes and then extracted node data to train the optimization algorithm. [PERSON] et al. [14] proposed a nested stacked genetic algorithm (NSGA) to solve the sensor position optimization problem in an existing bridge health monitoring system and established the finite element fishbone model of the Wuhai Yellow River cable-stayed bridge with ANSYS (R15.0). The main beam was divided into 259 nodes and 258 elements; then, the stiffness and mass matrices were extracted from the model as data for the subsequent optimization algorithm. [PERSON] et al. [15] proposed a hybrid optimization strategy named MSE-AGA in view of some defects in the existing bridge sensor arrangement methods, established a finite element model of a three-span suspension bridge with ANSYS, and optimized the sensor layout with the generated 1276 nodes as data. The sensor measurement point placement methods based on the optimization algorithm proposed by the above scholars can improve the identification ability of the actual dynamic characteristic parameters of the structure. However, in the selection of optimal measurement point training data, only the finite element model is used to select the target node data, resulting in the insufficient extraction of training data because the upper limit of selected data in this method is only about 100. Insufficient training data will have a great impact on the optimization results. Therefore, there is an urgent need for a method that can extract enough node data, effectively solving the problem of insufficient training data, is easy to operate, with fast extraction efficiency and high accuracy, and provides important basic support for the optimal arrangement of sensor measurement points. This paper requires a lot of node data in order to make sure that the sensors are arranged in the most critical positions of the structure and that the most accurate dynamic response of the structure is collected. Therefore, the tetrahedral mesh type is used and the structure is meshed with a smaller mesh size to get enough nodes. In recent years, many scholars have been interested in using swarm intelligence algorithms to solve the problem of sensor measurement point arrangement optimization in complex civil engineering fields. Inspired by the group foraging behavior of birds, fish and other creatures in nature, [PERSON] and [PERSON] [16] first proposed a new intelligent algorithm called the particle swarm optimization (PSO) algorithm in 1995. The algorithm has the advantages of few parameters, fast convergence, a simple program and a large search range, so it is widely used in neural network training, signal processing, the sensor arrangement of complex civil structures and other engineering fields. [PERSON] et al. [17] studied multi-vector particle swarm optimization (MVPSO) for solving single-objective optimization problems. [PERSON] et al. [18] proposed an advanced particle swarm optimization algorithm (APSO) to solve the flutter problem of long-span bridges and compared the flutter prediction results of the proposed method with the actual test results and obtained a good agreement. [PERSON] et al. [19] proposed a hybrid optimization algorithm combining particle swarm optimization and cuckoo search. The hybrid optimization algorithm was used to test the damage identification performance of a real steel-concrete composite bridge under temperature variation and verified its good robustness and engineering applicability. [PERSON] et al.