## 2 Review and Motivation
For example, an increase in the reliability index, as a percent of the hydropower supply, and a decrease in the vulnerability index, as a percent of the shortage in hydropower production, can be simultaneously defined as two objective functions. These algorithms are highly adopted, which allows all effective variables influencing the system, such as hydraulic or hydrological constraints, to be defined within these algorithms. In other words, the algorithms can be adapted based on the system problem with a specific condition. The convergence procedure and global solution achieved within the optimization process are very important for the system under study. To some extent, the system is required to be operated in real-time: Therefore, a fast convergence rate is needed. In addition, when the system is highly nonlinear and stochastic in nature, a searching procedure for the optimal solution might be trapped in a suboptimal solution. Accordingly, there is a need to look for an optimization algorithm that could have the potential to encounter a high convergence rate and stochastic search procedure within its mathematical process. In addition, the algorithm should encounter advanced operators that could have a high chance of achieving a global solution within the searching domain. In optimization theory, crows are the searchers: The environment can be considered the search space, and random storing positions can be taken as feasible solutions. The source that is best among all food sources is taken as the global solution, and the objective function is defined by the quality of the food source. By simulating the intelligence behavior of crows, the crow algorithm (CA) attempts to find the optimal solution to various optimization problems. The novelty of the current research paper is not only the investigation of the potential of a new optimization algorithm, but also the application of the algorithm in a relatively complex reservoir system with multidimensional features for multireservoir systems under different climate condition scenarios. In fact, most of the previous research efforts have focused on the optimization algorithms' performance, considering single reservoirs under single scenarios of climate change [10; 11; 12; 13; 14; 15; 16; 17]: In addition,the evaluation of those optimization algorithms was basically based on common performance indices that might provide improper judgment on the real model performance. In fact, there are a few statistical indices that are usually used for evaluating the performance of optimization algorithms, such as reliability, resilience, and vulnerability. Reliability is the probability that the reservoir can supply the volume of water specified by the demand target in any given year. Resilience, on the other hand, is taken to be the conditional probability of experiencing a period of no failure (no water deficit) given that a failure occurred in the previous time period. The use of the resiliency measure allows the concept of resilience, which is the ability of the system to recover from a failure, to be more fully expressed. The final measure of performance is the vulnerability of the system. Vulnerability is the maximum deficit experienced in any one year. Meanwhile, in the current research, different indexes for comparing different algorithms have been proposed. In this context, the present study attempts to investigate multireservoir performance under different climate conditions considering different reservoirs' inflow scenarios (dry, wet, and normal conditions). Most of the existing research works have shown the superiority of the proposed optimization algorithms for single reservoir systems under a normal scenario only, which might be insufficient and inadequate for generalization to all cases of reservoir systems and under different scenarios of climate conditions.