## 1 Introduction  
The actual flight operational loads that are experienced by an aircraft play a crucial role in evaluating its fatigue life and structural integrity [1; 2; 3]. To measure the load environment, operational load monitoring devices are installed on an aircraft, and typically the strain gauge sensors are utilized for operational load monitoring. Their placement significantly affects the accuracy and reliability of the measurements, as the incorrect placement of strain gauges can lead to misinterpretation of stress distributions [4; 5]. In order to acquire or monitor accurate aircraft operational loads, numerous sensors should be installed, as the more strain gauges installed on the aircraft, the more comprehensive the acquired aircraft operation load [6; 7]. However, it is not realistic to install numerous stain gauges on an aircraft due to reasons such as sensor installation technology and increasing aircraft weight [8; 9]. Moreover, a large number of strain gauges will generate a large amount of monitoring data, which will make it difficult to analyze and use the information of the aircraft structure effectively. In addition, if the positions of the strain gauges on the actual aircraft structure are not reasonable, it will not be possible to obtain the most effective monitoring data for the representation of the aircraft's health state, which will decrease the accuracy and effectiveness of the structural health monitoring system [10; 11]. Optimal placement minimizes the number of gauges needed, reducing costs while maintaining measurement accuracy [12; 13]. Therefore, reasonable strain gauge monitoring locations for accurate operational load monitoring is a key problem that should be addressed first. Recently, more and more researchers are becoming attracted to investigating the monitoring location optimization of structural sensor networks and have proposed a variety of methods for sensor network layout optimization [14; 15]. Different strain gauge location optimization methods have their advantages and application scopes. Currently, the research objects of sensor network optimization mainly focus on structures such as bridges and large-scale buildings, and most of them are based on the dynamic characteristics of the optimal layout [16; 17; 18]. The optimization of strain gauge sensors for vibration modes, structural deflection, and damage identification and assessment has been widely investigated [19; 20; 21; 22]. However, the strain gauge network based on the statics/fatigue characteristics of aircraft structures is normally installed based on the force transfer path of aircraft structures and the engineering experience. Traditionally, strain gauge location optimization is achieved via optimization algorithms such as genetic algorithms or sensitivity analysis. The latter examines how variations in gauge placements affect measurement reliability [23]. Nevertheless, the optimization of strain sensor networks for an aircraft is rarely investigated [24; 25]. In reference [24], a heuristic approach (genetic algorithm) with repeated deterministic local optimization was proposed for strain gauge placement optimization. A genetic algorithm is powerful optimization tool, but it also comes with several drawbacks, like slow convergence, dependence on fitness function, limited representation capability, or a lack of physical explanation. In reference [25], the strain monitoring locations are determined based on engineering intuition, which may not be sufficient. Moreover, feature selection and feature extraction can be harnessed for strain gauge location optimization [26; 27; 28]. Feature extraction is the process of decreasing the dimension of the measured datasets. Projection approaches like principal component analysis (PCA) [29] or the unsupervised clustering algorithm [30] are normally adopted. Feature selection involves choosing a small number of useful features from numerous features [31].