The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

The optimal control problem of reservoir group flood control is a complex, nonlinear, high-dimensional, multi-peak extremum problem with many complex constraints and interdependent decision variables. The traditional algorithm is slow and easily falls into the local optimum when solving the problem of the flood control optimization of reservoir groups. The intelligent algorithm has the characteristics of fast computing speed and strong searching ability, which can make up for the shortcomings of the traditional algorithm. In this study, the improved sparrow algorithm (ISSA) combining Cauchy mutation and reverse learning strategy is used to solve the flood control optimization problem of reservoir groups. This study takes Sanmenxia Reservoir and Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object and Huayuankou as the downstream control point to establish a joint flood control optimization operation model of cascade reservoirs. The results of the improved sparrow algorithm (ISSA), particle swarm optimization (POS) and sparrow algorithm (SSA) are compared and analyzed. The results show that when the improved ISSA algorithm is used to solve the problem, the maximum flood peak flow of the garden entrance control point is 11,676.3 m³, and the peak cutting rate is 48%. The optimization effect is obviously better than the other two algorithms. This study provides a new and effective way to solve the problem of flood control optimization of reservoir groups.

The operation of reservoir flood control operation is a multifaceted engineering issue characterized by complexity, several stages, nonlinearity, and many dimensions. It involves various intricate constraints and interrelated decision variables. Traditional algorithms tend to be slow and prone to local optima when solving optimization problems for flood control in reservoir groups. In recent years, with the introduction of various optimization technologies, more intelligent algorithms have been applied to optimize reservoir flood control scheduling problems in recent years. However, this remains a challenging task for large-scale reservoir group optimization scheduling problems. This work utilizes an Improved Grey Wolf Optimisation algorithm (IGWO) that incorporates Levy fly and random walk techniques for more effective optimization and scheduling of reservoir groups. Taking the Xiaolangdi Reservoir, Sanmenxia Reservoir, Luhun Reservoir, and Guxian Reservoir in Yellow River's middle and lower reaches as examples, a flood control dispatch system composed of four series and parallel reservoirs and a downstream control point at Huayuankou is studied as an example. We have established a flood control optimization scheduling model based on the Huayuankou control object, which maximizes the reduction of peak flow, and compared and analyzed the optimization results of the Improved Grey Wolf Algorithm (IGWO), Grey Wolf Algorithm (GWO), and Particle Swarm Optimization Algorithm (PSO). The results show that the Improved Grey Wolf Optimization algorithm achieves the best performance in calculating the maximum peak flow rate at Huayukou, with a peak flow rate of 18,681.1 m3/s and a peak reduction rate of 50.68%. This research offers novel perspectives and methodologies for addressing the optimization scheduling of reservoir clusters in flood control operations.