## 2 Review and Motivation  
Hydropower production based on a multireservoir system is a complex problem when considering nonlinear objective functions and different constraints. Therefore, the extraction of an optimal solution and hydropower management based on released water from upstream dams are real challenges. Thus, solving such problems causes other researchers to try and understand different strategies for these complex problems. Several efforts have been developed to achieve optimal hydropower generation by utilizing different metaheuristic algorithms. An optimization model has been developed to achieve reservoir operations with the aim of increasing hydropower generation while considering the environmental demand supply. The improved genetic algorithm (GA) was used for reservoir operations, and different flows in river regimes were considered for the optimization process. Nonlinear rule curves based on the GA were extracted for hydropower production, and the improved algorithm based on the elitism concept achieved more hydropower generation without trapping local optimal solutions [8; 9; 10]. Then, [PERSON] and [PERSON] [11] used the improved particle swarm optimization (PSO) to increase hydropower production in multireservoir systems. The new PSO was generated based on the modification of acceleration coefficients in the PSO. The minimum required water amount for environmental demands was supplied, and the hydropower generated reached hydropower plant capacity based on thenew PSO. In addition, an improved PSO was used for maximizing hydropower production and minimizing irrigation deficits at a large scale in China. The results indicated that the improved PSO based on the correction of the inertia coefficient had faster convergence over other versions of the PSO. Additionally, hydropower production based on an improved PSO reached hydropower plant capacity [12]. Furthermore, multireservoir operations based on a hybrid PSO and GA optimized performance with the aim of increasing hydropower production. The results indicated that the new hybrid algorithm could achieve more hydropower generation and faster convergence by covering the weak points of the GA and PSO based on increasing the diversity in the population [13; 14]. The Bat algorithm has been used for increasing the annual benefit based on hydropower production in multireservoir systems and hydropower plants [15]. Released water was considered to be the decision variable, and the results indicated that the generated benefit based on hydropower production via the Bat algorithm had more value than the benefits via the GA and PSO. The Bat algorithm was performed based on the echolocation ability during a Bat's life. In recent years, [PERSON] et al. [16] have used the water cycle algorithm (WCA) for a 10-reservoir system, and the results indicated that the new algorithm decreased computation time with the inception of water-drop movement into the environment: Additionally, released water was considered to be the decision variable, and storage was considered to be the state decision. The position of water drops in rivers and seas was considered the decision variable for this algorithm. The biography-based optimization algorithm (BBA) has been used for the optimization of a four-reservoir system, and the aim of the problem was increasing the generated benefit based on generated hydropower [17]. Moreover, the firefly algorithm (FA) has been applied to multireservoir systems with the aim of increasing hydropower production [18]. In addition, the monarch butterfly (MBF) algorithm and krill algorithm (KA) have been applied for optimized hydropower generation based on the optimal operation rule for either single or multiple dam and reservoir systems [7; 19]. The inception of swarm intelligence has caused algorithms to be directed toward the best solution to a problem with a minimum computation time. Problems with one or several objective functions can be simultaneously solved with such algorithms.