## 1 Introduction  
As more and more large-scale space engineering structures are put into use, it has become a hot topic for engineers and scholars in various countries to establish a matching structural health monitoring system for all-around safety assurance, and one of the most important aspects is how to effectively identify damage to space structures, including damage localization and quantitative analysis of the damage degree. Damage identification based on the difference in the dynamic properties of the structure before and after damage has been a hotspot of concern in the engineering community. Related technology applications in the field of engineering are numerous; the most famous use of the technology is the 101 building in Taipei, China [1]. This type of strategy determines the location and extent of damage by evaluating the correlation of the dynamic parameters of the healthy and damaged structure, which is essentially an inverse problem, and the finite element model updating technique is often applied to solve this type of inverse problem. Damage identification methods based on finite element model updating can transform the damage identification problem into a constrained optimization problem, which generally starts with the establishment of an objective function for evaluating the structural relevance of health and damage, and this is subsequently solved using an optimization algorithm to find an optimal solution. However, traditional optimization algorithms, such as the most rapid descent method and the conjugate gradient method, are not only slow to converge but also prone to fall into local optima. Therefore, it is significant for damage identification field to develop and search for more powerful optimization algorithms. With the rapid development of intelligent optimization algorithms in recent times, more and more scientists have studied this in depth and used it to solve optimization problems in damage identification. [PERSON], [PERSON] et al. [2] utilized an improved cuckoo algorithm, which is an algorithm combining frequency and vibration factors that has a strong global search ability but needs to search parameters manually, for damage identification in benchmark frames with significant results. [PERSON], [PERSON] et al. [3] proposed a hybrid genetic and particle swarm algorithm for damage identification in damaged thin-plate models and achieved good results; the algorithm was found to have strong search capabilities and a fast convergence speed, although it takes a long time for a single iteration. [PERSON] et al. [4] proposed a two-stage damage identification method based on the augmented whale algorithm to successfully identify multiple occurrences of damage in simply supported beams with thin plates; this is a new algorithm with strong search capabilities and robustness, although the accuracy is not as good as the best algorithms. [PERSON] et al. [5] successfully localized and quantified the damage in beams and planar frames using a damage detection technique based on the frequency response function and particle swarm algorithm, but some evaluations of the performance of this technology were missing. [PERSON], [PERSON] et al. [6] proposed a hybrid stochastic/deterministic optimization algorithm for damage identification, which is more accurate and efficient in identifying damage when compared with genetic, harmonic, and particle swarm algorithms. [PERSON] et al. [7] proposed a damage identification method based on the artificial bee colony algorithm with a hybrid search strategy and modal data and successfully identified damage in a truss model and a plate model; they were also able to achieve a balance between global and local searches to avoid falling into an local optimal solution with dependencies on parameter selection. [PERSON] et al.