###### Abstract
[10] combined the BP neural network genetic algorithm to optimize the centrifugal pump and improve the hydraulic performance of the pump. [PERSON] et al. [11] applied a neural network and the NSGA-II genetic algorithm to slurry pumps to achieve the optimal design of efficiency as well as the high efficiency zone of this pump. [PERSON] et al. [12] optimized the vane inlet and outlet angles based on a genetic algorithm with efficiency as the optimization objective, which led to a 4.5 percentage point increase in pump efficiency and a significant expansion of the high efficiency zone, as well as a 0.83 m increase in the pump head. [PERSON] et al. [13] optimized a fuel pump using a genetic algorithm combined with an adaptive weighting method, with impeller structural parameters as the optimization variables and volumetric efficiency and outlet flow rate as the optimization objectives, which resulted in a 3.9 percentage point increase in outlet flow rate and a 0.53 percentage point increase in volumetric efficiency. [PERSON] et al. [14] took a double-vane sewage pump as the research object and used a combination of neural network and particle swarm optimization to optimize the design, and after experimental verification, the optimized pump head efficiency was improved to some extent. [PERSON] et al. [15] took a model of a low specific speed pump as the research object, with the stability of the operating process, anti-cavitation performance, and energy loss as the optimization targets, and used a genetic algorithm to calculate the solution. After optimization, the pump pressure pulsation amplitude was reduced, and the stability improved to some extent. [PERSON] et al. [16] used a cyclone pump as the research object, with the width of the non-vane cavity, the number of vanes, and the vane outlet width as the optimization variables and the efficiency and vane surface shear stress as the optimization objectives. They used a genetic algorithm and Kriging model to calculate the solution, which improved efficiency and reduced the average vane shear stress. [PERSON] et al. [17] selected three optimization variables, namely impeller outlet width, diameter, and angle, and combined the Latin hypercube sampling method, BP neural network, and NSGA-III algorithm to optimize a model of a low specific speed centrifugal pump with pump head and efficiency as the optimization objectives. [PERSON] et al. [18] optimized the geometric parameters of the vane profile of a centrifugal pump based on an intelligent algorithm, and the numerical simulation and experimental values of pump head efficiency were improved after optimization. [PERSON] et al. [19] studied a water jet propulsion pump with lift resistance ratio and pressure as the optimization objectives and carried out optimization based on an improved particle swarm optimization algorithm, after which the lift resistance ratio was improved by 14.7 percentage points and the minimum pressure was increased by 20%. [PERSON] et al. [20] took a mixed-flow pump as the optimization object, selected the vane load as the optimization variable, 0.7 times the standard operating condition to 1.1 times the standard operating condition high efficiency zone as the optimization objective, and used the Kriging approximation model and NSGA-II algorithm for optimization. However, most of the optimization is carried out by a genetic algorithm, and there is less research on the application of neural network prediction models combined with multi-objective particle swarm optimization to the optimal design of submersible well pumps. The research and analysis of pump performance optimization mainly include the following: studying the impact of a single factor on pump performance; optimizing the theoretical formula of pump performance; applying experimental design methods to optimize pump performance; combining experimental design and an approximate prediction model [21]; and using intelligent optimization algorithms to directly search for the optimal computational solution.