### Literature Review  
Since the issue of OSP was raised, numerous scholars have studied and proposed many classical methods. In 1991, [PERSON] [15] proposed the effective independence (EI) method, which achieved the best estimation of modal parameters with as few sensors as possible by eliminating test points that contribute less to the target modal partitions among the candidate points. [PERSON] et al. [16] proposed the modal kinetic energy (MKE) method, which arranged the measuring points in each degree of freedom (DOF) of the structure in the order of modal kinetic energy to select the sensor locations with larger vibration energy. [PERSON] et al. [17] compared the intrinsic connection between the modal kinetic energy (MKE) method and the effective independence (EI) method, proposing an alternative form of the two effective computational iterative EI methods. [PERSON] et al. [18] demonstrated that the effective independence driving-point residue (EI-DPR) method provided an effective method for OSP in identifying the vibration characteristics of bridges by studying six different sensor placement techniques. [PERSON] et al. [19] proposed a fast and efficient independence method based on QR decomposition, which greatly improved the computational efficiency without the need to compute the information matrix of the structure. [PERSON] et al. [20] proposed a novel multi-factor OSP strategy, namely, effective independence-acceleration amplitude-total displacement (EI-AA-TD), to formulate a multifactor optimization framework for sensor placement. The above classical methods are effective, but often the results obtained are not optimal solutions. Various intelligent algorithms have been applied to OSP to address the shortcomings of some classic algorithms [21]. [PERSON] and [PERSON] et al. [22] proposed an improved adaptive genetic algorithm (IAGA) based on the modified modal assurance criterion and verified the effectiveness of the method in sensor placement for a whart structure. [PERSON] et al. [23] introduced an adaptive monkey algorithm (AMA) to solve the sensor placement problem for target locations under the constraints of computational efficiency and convergence stability. [PERSON] et al. [24] proposed an improved partheno-genetic algorithm (IPGA) based on the initial layout of sensors and applied it to a quayside container crane. [PERSON] et al. [25] chose an evolutionary optimization algorithm (EOA) based on particle swarm optimization (PSO) and validated it through a 4-story tall RC building, a footbridge, and a historic masonry church damaged by earthquake actions. [PERSON] et al. [26] proposed a novel triple-structure coding approach and implemented the encoding scheme using two of the most famous evolutionary algorithms, the genetic algorithm (GA) and the mutated particle swarm optimization (MPSO) algorithm, to address the practicality of modal identification in high-rise structures and steel dome trusses, demonstrating progress in cost reduction in the context of sensor layout optimization. [PERSON] et al. [27] used an exhaustive search method, a heuristic search method, and a genetic algorithm based on Bayesian optimal sensor placement strategy to determine the optimal (and worst) position of sensor clusters for AE source localization in isotropic plates with unknown material properties. To strike a balance between sensor cost and real-time performance, [PERSON] et al. [28] proposed a new optimal sensor placement (OSP) method. A two-stage OSP method based on the hierarchical ranking evolutionary algorithm (HREA) has been developed, and four classic binary multi-objective optimization algorithms have been introduced for comparison. In addition, the AHA algorithm, although it falls under the category of meta-heuristic algorithms, is significantly different from existing algorithms.