Abstract: For solving the optimization operation model of parallel pumping stations, wolf pack algorithm (WPA) has some problems such as poor convergence and robustness. In order to improve these problems, taking a typical parallel pumping station group as an example, aiming at the lowest energy consumption of the main unit of the pumping station system, and considering the constraints of flow rate, blade angle and number of running units, a mathematical model for optimal operation of parallel pumping stations was established in this paper. Simulated annealing algorithm (SA) was introduced into WPA, named as hybrid wolf pack algorithm (HWPA), which was proposed to solve the established optimization model. Minimum value, average value and standard deviation of energy consumption were used to evaluate the performance of the algorithm. Compared with particle swarm optimization (PSO) and WPA, the minimum value of energy consumption based on HWPA was decreased by 15.60 kW and 10.23 kW on average of energy consumption, the average value of energy consumption was decreased by 36.94 kW and 14.30 kW on average respectively, and the standard deviation was decreased by an average of 84.82% and 72.90% respectively. On the basis of the HWPA, four parameters of walking step, running step, minimum and maximum of siege step in the algorithm were analyzed by single factor simulation. At the same time, the min-max standardization method was used to standardize the minimum value, average value and standard deviation of energy consumption. The standardized value was further weighted to get the comprehensive evaluation index(E) of the algorithm evaluation. Then, according to the trend of E, the reasonable range of the above four parameters was determined. According to the results of single factor analysis, the four parameters mentioned above were selected as independent variables, and latin hypercube sampling was used to design simulation. Considering the minimum value, average value and standard deviation of energy consumption, the optimal combination of parameters was determined to be 0.33, 1.53, 0.672 and 4.8×105, and then the improved hybrid wolf swarm algorithm (IHWPA) was proposed. Compared with HWPA, the minimum and average value of energy consumption based on IHWPA were reduced by 4.66 and 13.26 kW on average, and the standard deviation was reduced by an average of 94.02%. IHWPA was used to determine six optimization schemes of typical parallel pump stations under different operation conditions. The results showed that the global convergence and calculation robustness of the algorithm were improved by introducing SA algorithm and optimizing the parameters of WPA. The optimal scheme reduces the energy consumption by 9.80% on average compared with the actual operation scheme, and the energy saving effect was significant. When the total pumping flow rate was small, the optimization effect of the optimization scheme was more significant, conversely, the difference between the two schemes became smaller, but the energy consumption of the optimization scheme was still lower than that of the actual scheme. It can be concluded that IHWPA is suitable for solving optimal models for this kind of pumping stations, which can provide a reasonable and effective operation scheme for pumping station engineering and reduce the operation energy consumption.