## 1 Introduction  
Flood disasters are currently among the major global problems faced by human society. From 1989 to 2018, 3945 major flood disasters occurred around the world, with China, India, the United States, and Indonesia experiencing the largest number: about 1200 in total [1]. There were 109 flood disasters worldwide in 2018, causing 1995 deaths, affecting 12.62 million people, and resulting in $4.5 billion in direct economic losses [2]. Although global flood deaths and affected populations have shown a continuous downward trend over the past 30 years, economic losses have shown an upward trend. Owing to the frequency of and significant economic losses associated with flood disasters, a considerable number of water conservation projects have been undertaken to reduce the adverse effects of floods. Among these, reservoirs are created by constructing a dam across a river. However, with ongoing socioeconomic development, the purpose of the reservoir has expanded from guaranteeing flood control safety of the river to including the provision of power generation, water supply, irrigation, ecological environment maintenance, navigation, sediment control, recreation, fisheries, etc. As of April 2020, 58,713 large dams have been constructed worldwide [3]. According to a report by the World Commission on Dams, the improvement in operation and maintenance of existing dams offers opportunities to address local or regional development and to minimize social and environmental impacts [4]. To do so, it is necessary to implement a scientific and reasonable reservoir flood control operation strategy. Thus, the goal of reservoir flood control operation studies is to define an optimal operation policy for a given reservoir that balances its various purposes [5]. This policy represents a powerful tool for the guidance of reservoir operation, serving as not only a decision-making reference during the planning and design of a water conservancy project but also a key to realizing the comprehensive benefits of the reservoir during its operation. Existing reservoir flood control operation methods can be roughly divided into two groups: conventional operation methods and algorithmically optimized operation methods. Conventional reservoir operation methods are semi-empirical and semi-theoretical and are presented in the form of flood control operation graphs or tables. During real-time flood control operation, operational decisions (e.g., reservoir discharge or hydroelectric power generation) during each period are specified as a function of the appropriate available information (e.g., the current or previous reservoir water level, current or previous reservoir inflow, and time of year) [6]. Such conventional methods have been widely used in reservoir operations owing to their intuitive and practical structure; however, they often fail to consider the latest operational data or to address the complex nonlinearity that exists between the relevant dependent and independent variables [7]. Thus, as the complexity and interdependency of the systems considered in reservoir management increase, it becomes more difficult to obtain an optimal operating scheme using conventional methods. Optimization algorithms have therefore been increasingly applied to formulate reservoir flood control operation strategies, effectively addressing the shortcomings of conventional methods [8]. In the past decades, a wide range of optimization algorithms have been proposed that can generally be classified into conventional optimization algorithms and heuristic intelligent algorithms. Conventional optimization algorithms include linear programming [9; 10; 11], nonlinear programming, dynamic programming (DP) [12; 13; 14], and progressive optimality algorithm (POA) [15; 16] approaches as well as various improvements thereof, such as the multi-stage DP [17; 18], incremental DP [19; 20], stochastic DP [21], parallel DP [22], and DP combined with POA (DP-POA) method [23].