## 1 Introduction
### Background  
One of the critical issues in water resource management is reservoir operation, which requires that both downstream demands are met and that reservoir storage is adequate for critical periods [7; 8; 9; 10; 11]. The challenges of a reservoir operation could be solved by an optimization framework. Different methods exist for solving reservoir operation issues in water resources management [12; 13]. Traditional methods, such as nonlinear programming, linear programming, and dynamic programming, have been used in previous studies [14; 15; 16]. Recently, new methods, such as evolutionary algorithms and metaheuristic algorithms, have been used for reservoir operation [17]. [PERSON] [18] used a genetic algorithm for reservoir operation in India. The aim of the study was to meet downstream irrigation demands, and the results showed that the genetic algorithm did effectively satisfy these demands better than dynamic programming methods. [PERSON] et al. [19] used the honey bee mating optimization method for reservoir operation, and the results showed that the method converged faster than the genetic algorithm. [PERSON] et al. [20] also used the honey bee mating optimization algorithm for multi-reservoir operations, and the results showed that the average solution of the method was close to the global solution of the problem. [PERSON] and [PERSON] [21] used the genetic algorithm for a multi-reservoir system with multiple purposes. Results showed that the genetic algorithm yielded a higher reliability index and adequately satisfied different demands. [PERSON] et al. [22] used genetic programming and a genetic algorithm to determine a water release policy. Results showed that the genetic programming method met all requirements, with low vulnerability among different demands. The bat algorithm, one of the more common methods, was also utilized by [PERSON] et al. [23] for power plant operation. Results showed that the bat algorithm was superior to other metaheuristic algorithms in finding optimal solutions for more power generation. Thus, different studies have shown that various evolutionary algorithms displayed high performance in solving complex problems. However, these studies did not consider the crucial effects of climate change or reservoir operations, and few studies have found the impact of climate change on reservoir operations. [PERSON] et al. [24] optimized the performance of an irrigation system based on genetic programming in the context of climate change. The period of 1987-2000 for Aidoghmoush dam in East Azerbaijan, Iran was considered the base period for their study, and the period 2026-2039 was considered the future period. Results indicated that the runoff volume and reservoir inflow decreased in the forthcoming period and that future demand also increased. Thus, a notable deficit was observed in the future period. [PERSON] et al. [25] optimized multi-reservoir operations of the Karoon Reservoir System to increase power generation in base and future periods, with the results indicating that the release of water in the future period (2011-2030) decreased because inflow to the reservoir decreased. Additionally, power generation in the future period was less than that of the base period (1971-1990), and the released water was considered a decision variable. [PERSON] et al. [26] extracted the trends from a base period and future period to increase power generation at a power plant of Karoon-4 reservoir in Iran. Results indicated that the vulnerability index of the coming period increased while the reliability index decreased based on a multiobjective genetic algorithm. Moreover, inflow to the reservoir significantly decreased due to lower runoff volume, and subsequently, power generation in the future period experienced considerable issues. [PERSON] et al. [27] used a particle swarm algorithm to provide an optimal solution for decreasing the deficit of an irrigation system in base and future periods on Lake Karla Watershed.