# A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection
## 1 Introduction
By continuous optimization and adjustment of the physical parameters of the benchmark finite element model, the model updating method is widely used based on the static or dynamic test data of the structure [29; 30]. Depending on this, a nonlinear objective function is established according to the parameter changes before and after the updating of the finite element model, which is transformed into a mathematical function optimization problem. Then, the position and degree of structural damage can be detected by optimizing the mathematical function [31]. Structural damage detection based on model updating requires establishing a precise benchmark finite element model, which usually contains many degrees of freedom and multiple correction parameters, resulting in a high-dimensional multi-extremum optimization function [32]. Traditional mathematical function optimization methods are challenging to solve such complex mathematical models. To optimize such mathematical models quickly and accurately, some scholars have introduced the heuristic intelligent algorithm into structural damage detection based on model updating [33; 34]. [PERSON] et al. [35] developed a new method of densely connected convolutional networks, which improved the accuracy of structural damage identification. [PERSON] et al. [36; 37] introduced a variational Bayesian inference approach and phase space embedding with the stochastic Koopman operator to identify structural damage. [PERSON] et al. [38] combined modal strain energy changes and general neural networks to identify structural damage. [PERSON] et al. [39; 40] proposed an indirect structural damage detection strategy and a two-stage structural damage identification method. [PERSON] et al. [41] adjusted the inertia weight factor of particle swarm optimization by sigmoid function, which improved the identification accuracy of structural damage identification. [PERSON] et al. [42] introduced the sunflower algorithm into structural damage detection to enhance the robustness of structural damage detection. [PERSON] et al. [43] combined the firefly algorithm with the Nelder-Mead algorithm to detect structural damage. [PERSON] et al. [44] used the modal strain energy ratio as a damage factor to identify the location and degree of structural damage in stages through the slime mould algorithm. [PERSON] [45]proposed a structural damage identification strategy based on an accelerated differential evolution algorithm. [PERSON] et al. [46] implemented structural damage identification in stages by auto-associative neural networks and Levenberg-Marquardt neural networks. [PERSON] et al. [47] combined partial modal mode shapes and natural frequencies to identify structural damage through artificial intelligence neural networks. [PERSON] and [PERSON] [48] proposed a structural damage detection strategy based on an adaptive covariance matrix. [PERSON] and [PERSON] [49] combined the flexibility matrix with the modal strain energy to offer an improved teaching-learning optimization algorithm. [PERSON] and [PERSON] [50] combined the probabilistic neural network and data fusion technology with correlation fractal dimension to identify structural damage. However, for some high-dimensional swarm intelligence algorithms, the convergence speed and identification accuracy can still be improved. Then, the chimp algorithm is introduced as an attempt to solve the optimization problem of structural damage detection. Inspired by the hunting behavior of the chimp population, the Chimp Optimization Algorithm (ChOA) was first proposed by [PERSON] and [PERSON] in 2020 [51]. ChOA is a heuristic intelligent algorithm with a fast global search ability, which can be used to solve optimization problems. Compared with other similar algorithms, ChOA has fewer parameters and is easier to improve. However, ChOA still suffers from the problem of unbalanced exploration, which leads to premature traps and insufficient identification accuracy. In this regard, some scholars have proposed a few improvement strategies for this method.