## 1 Introduction
In the early days, researchers focused on analyzing a single aircraft parameter to investigate its impact on the range, lift-to-drag ratio, and other aspects of performance [15]. The control variable method was widely adopted, especially in the research about range. To study the relationship between the range of a hypersonic vehicle and the aircraft parameters, the Bruguet formula was employed [16; 17], and some methods, such as the cell mapping method in [18], were applied as well. Moreover, the aerodynamic layout of the hypersonic vehicle was analyzed to clarify the influence on the lift-drag ratio and other indicators [19; 20]. However, due to the strong interaction between the aircraft parameters, the hypersonic vehicle is a complex system. The analysis and optimization of the aircraft parameters cannot focus only on a single factor. The coupling relationship between various parameters also needs to be taken into consideration. Therefore, in recent years, surrogate model technology has been widely employed in the analysis of aircraft parameters. The relationship between the aircraft parameters and performance indicators can be described by a surrogate model, and then parameter optimization can be carried out based on the surrogate model [21]. Currently, to establish a surrogate model, polynomial regression, the Kriging model, radial basis function, and a neural network can be employed [22]. In addition, many statistical methods have been introduced into the research based on the surrogate model. In the analysis of the aircraft parameters, the sensitivity analysis method [23] and correlation analysis method were widely used to dig out important parameters whose influence was most obvious [24; 25]. The orthogonal test and some novel methods, such as the technology identification, evaluation and selection method in [26] and hybrid algorithm in [27], were employed to explore an optimal design space and parameter estimation in aircraft parameters analysis as well. Based on the surrogate model, optimization methods have been widely employed in the parameter optimization research [28]. There are various parameter optimization methods, which can be mainly divided into gradient-based methods and the intelligent method. With the development of computing science, the intelligent method increasingly plays an important role. A genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE) algorithm, and so on have been widely developed [29]. For an optimization problem with high dimensions, the PSO algorithm is demonstrated to be suitable and is also easy to program, so a great deal of research has adopted it [30,31]. In this paper, in order to describe the influence of aircraft parameters on the optimal cruise point, a sensitivity analysis and surrogate model technology with the optimization method is employed. Firstly, based on a parametric hypersonic vehicle model, the position of the optimal cruise point is quickly solved by the PSO algorithm. On this basis, the influence law of a single aircraft parameter on the optimal cruise point is explored, and a model about the relationship between the aircraft parameters and optimal cruise point is established by neural network. Based on the model, the aircraft parameters are optimized under various constraints to adjust the optimal cruise point to the given position. The aircraft parameters optimization method proposed reveals how the aircraft parameters should be adjusted, which is able to provide guidance in the design of a hypersonic vehicle.