Due to long life and sedimentation, the capacity of Voshmgir reservoir has decreased. Therefore, it is important to think about how to save capital and reduce the shortage volume. The main purpose of this investigation is to estimate the amount of shortage volume of voshmgir reservoir by considering the two-reservoir system in Golestan province. In this research, for simplifying, the node method was used. The optimization of problem was done using linear programming method. By considering the importance of evaporation in reservoirs, the results showed that in optimum operation of Voshmgir and Golestan reservoirs, the evaporation decreased and in present situation and future condition, the Voshmgir reservoir capacity has to be increased by 9 million cubic meters to supply all water demands. The results showed that the linear programming was very useful method in water resources problems.

Almost all real-world decision-making issues, especially in the water resource management area, are multi-objective issues that incorporate different and conflicting objectives. Due to the wide range of such applications, different models have been proposed to tackle multi-objective optimizations, among them NSGA-II and MOPSO are the most important models. The purpose of this study is to compare the performance of NSGA-II and MOPSO algorithms in solving multi-objective optimal operation of a hydropower reservoir. The results showed that by reducing the minimum daily energy production from about 1040 MW to 650 MW, we will meet an increase in the revenue of about 10% of the total annual revenue.

Traditional management of water resources, which is often based on cost and benefit, is not sustainable approach; therefore reassessment of the former approaches seems necessary nowadays. Optimum reservoirs operation is one of the important water resource management problems. Present study has used the non dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of Sefidrud reservoir in North of Iran. The main goal of the study is to reach sustainable operation of the reservoir by considering these objectives simultaneously: supply of downstream demand, sediment evacuation and hydropower generation.

In this research, the simulation-optimization method was used with the integration of the WEAP simulator model and the multi-objective gray wolf optimization algorithm (MOGWO) for the optimal exploitation of the Dez Dam water resource systems. The main goal in this structure is to provide a solution in which, based on the resources and costs of the region, in addition to reducing the penalty due to the violation of the authorized capacities of the reservoir, the percentage of meeting the needs of various uses in the entire period, especially in the dry months, will also increase. The results showed that in the reference scenario, in many dry years, the percentage of meeting the demand in critical and low water months was close to zero. This will create instability in the system and create irreparable economic losses and will have many social consequences. Optimizing the system based on the proposed structure in this research has improved the percentage of demand supply in critical months so that the minimum percentage of demand supply in these months reaches 30% and there is no month with zero demand supply percentage. The proposed model in this research can lead to better management of the reservoir in dry areas by taking into account the allowed capacities of the reservoir with optimal release of flow in high water seasons and storing part of the flow in the reservoir and releasing it in low water seasons.

In this research, after introducing the Whale, Cuckoo search and fruit fly Multi-Objective Optimization Algorithms, their performance individually and compared to each other was evaluated to optimize the Golestan Dam reservoir operation policies with an approach to reduce downstream water demand shortages and flood storage volume management based on reliability, resilience, vulnerability and sustainability criteria. The results showed that the proposed algorithms act differently in optimizing the objective functions. So that the whale multi-objective optimization algorithm leads to better values of objective functions. Regarding to the optimization time, although, there are no significant differences between the cuckoo search and whale algorithms, both perform far better than the fruit fly algorithm and achieve much more convergence over time. When comparing the whale algorithm, as the most efficient algorithm, with the fruit fly algorithm, as the most inefficient algorithm, the model reliability and resilience indices increased by 44% and 52%, respectively, and its vulnerability decreased by 23%, indicating a better performance compared to the other algorithms. Therefore, Whale multi-objective optimization algorithm performs better and converges better than the other algorithms, and cuckoo search and fruit fly multi-objective algorithms ranked second and third.

Optimal operation of reservoirs is one of the most important issues in water resources management. In the present study after introducing Whale Optimization Algorithm (WOA), the performance of this algorithm is evaluated separately and in the hybrid with the Genetic Algorithm (hybrid WOA-GA) in the optimal operation problem of Salman Farsi dam reservoir. In the present optimization problem, the objective function is defined as minimizing the total deficit during the operation period. Also, the constraints of the reservoir continuity equation, reservoir storage volume and released volume from the reservoir have been applied to the objective function of the problem. The Performance of proposed algorithms is compared with the performance of the Genetic Algorithm (GA) and Non-Linear Programming (NLP). The performance of models has been evaluated based on Reliability, reversibility, vulnerability and stability criteria. The results of optimal solutions showed that the absolute optimum is equal to 0. 181 based on NLP method and using Lingo software and the optimal solutions for the models of the hybrid WOA-GA, GA and WOA with 2. 9, 24. 2 and 337 percent increase compared to the absolute optimum are ranked first to third respectively. A Multi-Criteria Decision-Making technique (MCDM) has been used to select the best model based on the objective function and evaluation criteria of the models' performance. The results of this technique showed that the performance of the hybrid WOA-Ga model is ranked first, and the models of NLP, GA and WOA are in the next ranks, respectively.

Nowadays, with increasing energy consumption costs, the optimization of energy efficient processes is being more considered than before. The essential part of the operational cost of water conveyance systems is energy consumption and maintenance costs of pumping stations. Therefore, it is necessary to develop an efficient method for optimal operation of these stations in order to reduce energy consumption and pumps maintenance costs. In this study, the optimal operation of pumping stations is considered as a multi-purpose optimization problem with the objective functions: 1- energy costs 2- maintenance costs. In order to resolve this problem, an optimization— simulation model based on Non-Dominated Sorting Genetic Algorithm (NSGA-II) is coupled to the EPANET hydraulic simulation model. The proposed model is applied to find the optimal pump scheduling program of Shiraz water conveyance system from Doroudzan Dam. Results revealed that by considering all different limitations of the problem, the energy costs were 30% and 5% less than the current state of operation and previous studies respectively. Also, the proposed model can decrease the cost of pumps maintenance by controlling the number of pump switches.

Due to population growth, shortage and severe constraints of water resources in the country, one of the main steps in management and planning of water resources is to optimize reservoirs and change the cultivation pattern. In this study, using 40 years discharge data of Kamish River and WEAP model, Hersin dam reservoir which is uder study, was simulated. Firstly, by considering different cultivated patterns, the reliability of domestic, industrial, agricultural, and environmental requirements were simulated by WEAP model and the proper crop pattern was selected on the basis of greater sustainability of the system. Then, optimization was performed by minimizing the shortages in downstream of the dam using LINGO model. According to the results, the annual average demands of environment, drip-irrigated gardens, sprinkler-irrigated lands and wastewater waterline, were estimated to be 98. 98, 87. 51, 89. 79, and 95. 63%, respectively, while these values obtained from the optimization model were 100, 99. 99, 99. 21, and 99. 12%, respectively, which indicates that the percentage of shortcomings is less than 1%. The average overflow volume in simulation and optimization models were 7. 31 and 4. 09 million m3/year, respectively, which indicates that the optimization model has a lower water lost than the simulation model. Hence, by appropriate planning and management, it is possible to deduct the amount of deficits significantly, especially in the sector of agriculture.

In this study, the performance of the algorithms of Whale (WOA), Differential Evolution (DE), Crow Search (CSA), and Gray Wolf optimization (GWO) were evaluated to operate the Golestan Dam reservoir with the objective function of meeting downstream water demands. After defining the objective function and its constraints, the performance of the algorithms was compared with each other and with the absolute optimal values obtained by GAMS nonlinear programming method (19. 41). These values together with each algorithm optimization results were ranked using six multi-criteria decision-making methods of TOPSIS, VIKOR, LINMAP, CODAS, ELECTRE I and Simple Additive Weighting after obtaining the performance evaluation indices of each algorithm (Reliability, reversibility, and vulnerability). Finally, integration methods (Mean, Borda, and Copeland techniques) were used to evaluate the performance of models. The results showed that the average responses of the GWO, WOA, DE, and CSA were 1. 08, 1. 49, 1. 29 and 1. 19 times the absolute optimal response and the answers’ coefficient of variation obtained by GWO was 2, 113 and 1. 43 times smaller than the WOA, DE, and CSA, respectively. Moreover, all integration techniques indicated the superiority of the GWO. Then, the CSA, DE, and WOA algorithms were ranked second to fourth, respectively. On the other hand, the use of these methods in solving the problem of Golestan Dam reservoir optimization was considered appropriate due to the similarity of the results obtained from the integration techniques with the results of TOPSIS, VIKOR and LINMAP methods.