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This research tries to find the best operation strategies for a reservoir system with the Flow Direction Algorithm (FDA), which was recently introduced. This study evaluates the implementation of the FDA, for the first time, for optimizing the hydropower operation of the Karun-4 reservoir in Iran for 106 months (from October 2010 to July 2019) and for the multi-reservoir systems for 12 months. Multi-Reservoir systems which are hypothetical 4 and 10-reservoir systems are studied to demonstrate the effectiveness and robustness of the algorithms. The results are compared to those of the three most commonly used evolutionary algorithms, namely the Particle Swarm Optimization Algorithm (PSO), the Weed Algorithm (WA), and the Genetic Algorithm (GA). The multi-reservoir results indicated that the absolute optimal solution was 308.292 in the four-reservoir benchmark system (FRBS) and 1194.441 in the ten-reservoir benchmark system (TRBS), and according to these results, FDA outperformed three other algorithms. In the Karun-4 reservoir, the best approach was chosen with the analytical hierarchy process (AHP) method, and according to the results, the FDA outperformed PSO, WA, and GA. The reliability percentage for FDA, PSO, WA, and GA was 95%, 86%, 78%, and 64%, respectively. The average optimal objective function value generated by FDA was 0.138, compared with PSO, WA, and GA, with the values of 0.322, 0.631, and 1.112, respectively, being better. The hydropower produced by FDA was more than three other algorithms in less time, with the lowest coefficient of variation value, which demonstrates the power of the FDA. [ABSTRACT FROM AUTHOR]

Flood is one of the natural hazards that its prediction and control is of great importance. One of the most important models in the field of flood routing is the Muskingum model. In this study, Muskingum four-parameter model is used for flood routing. The existence of unknown parameters causes the Kidney algorithm to be used as a new evolutionary algorithm based on reabsorption and filter operators for flood routing. The operators make the Kidney algorithm accelerate the convergence process and improve the quality of responses. Three floods were selected based on Kidney algorithm. The results indicated that the amount of sum squared deviation reduced by 84, 90, 35 and 86% for the Wilson flood algorithm compared to the Honey bee mating optimization, pattern search, particle swarm optimization (PSO) and harmony search (HS) methods for flood routing based on observational and simulated discharge. Also, the results indicated that the Kidney algorithm is more accurate based on the Muskingum four-parameter model than the Muskingum three-parameter model and the Muskingum two-parameter model. The sum absolute deviation value for the HS, genetic algorithm and PSO methods is 94, 88 and 82% higher than Kidney algorithm for Karahan flood. In addition, the predicted peak discharge for the Karahan flood and the predicted time for peak discharge were more accurate than other evolutionary algorithms. Also, the results of the Kidney algorithm for the Viessman and Lewis floods indicated that the Kidney algorithm well reduces the error indicators. Therefore, the Kidney algorithm as a suitable algorithm based on Muskingum four-parameter model had higher accuracy. [ABSTRACT FROM AUTHOR]

Lingering droughts and shortage of water sources signify the importance of optimal utilization of water reservoirs such as multi-reservoir systems. These systems could be employed not only as a storage system to manage the water utilization but also as a power generation system. To rise the generated power besides the management of water utilization, an optimization algorithm should be used. In this study, the kidney algorithm in three different scenarios, namely the wet, normal, and dry years is employed to fulfill such an engineering operation in a four-reservoir system in China. Simulations show well compatibility of the water level inside the reservoir with real statistical indices in terms of RMSE and MAE. Results also reveal that using the kidney algorithm not only reduces the required calculation but also increases the convergence pace with respect to other algorithms that have been used (bat, shark, abundance of particles, and genetic algorithms). Moreover, it increases the amount of the generated energy by a factor of 2.2–3.2 with respect to the aforementioned algorithms. Results indicate the capability of the kidney algorithm in the management of water sources and engineering operations. [ABSTRACT FROM AUTHOR]

This study employed a new evolutionary algorithm namely, the crow algorithm (CA), to optimize reservoir operation and minimize irrigation water deficit. Comprehensive analysis have been carried out between the proposed CA algorithm and other algorithms such as Prticle Swarm optimization (PSO), Shark Algorithm (SA), Genetic Algorithm (GA), and Weed Algorithm (WA). In addition, in order to select the optimal optimization algorithm among all of the investigated ones, a Multi-Criteria Decision model has been utilized. The time of computation was 45 s for CA but was 65, 50, 78, and 99 s for SA, WA, PSO, and GA, respectively. The CA exhibited greater volumetric reliability and a lower vulnerability index over the other examined algorithms. Furthermore, the Root Mean Square Error (RMSE) between demand and water release was 1.11 × 106 m3 for CA compared to 2.14 × 106 m3, 3.33 × 106 m3, 3.45 × 106 m3, and 3.78 × 106 m3 for SA, WA, PSO, and GA, respectively. Using a multi-criteria decision model based on different indices, including the vulnerability index, resiliency index and volumetric reliability index, CA was ranked first. [ABSTRACT FROM AUTHOR]

One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consume for convergence. Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. However, within the EA, there is a need to assume internal parameters at the initial stage of the model development, such assumption might increase the ambiguity of the model outputs. This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat–swarm algorithm (HB-SA). The main idea behind this hybridization is to improve the BA by using the PSOA in parallel to replace the suboptimal solution generated by the BA. The solutions effectively speed up the convergence procedure and avoid the trapping in local optima caused by using the BA. The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. The results showed that the proposed HB-SA algorithm can achieve minimum irrigation deficits during the examined period and outperforms the other optimization algorithms. In addition, the computational time for the convergence procedure is reduced using the HB-SA. The proposed HB-SA is successfully examined and can be generalized for several dams and reservoir systems around the world. [ABSTRACT FROM AUTHOR]

Considering the great importance of flood prediction, flood routing based on Shark Algorithm (SA) and Four-Parameter Nonlinear Muskingum (FPNM) has been proposed in the present study. In fact, the Muskingum model is considered as one the most efficient method for predicting flood. However, to successfully implement Muskingum Model, there is a need to compute various parameters of this model utilizing a lot of data that characterized the physical features of the catchment. Therefore, there is a need to integrate the Muskingum model with an optimization method. Nevertheless, there are several drawbacks including trapping in local optima, overhead response and convergence time-consuming have been experienced using the existing optimization methods. Therefore, in this study, a proposal for utilizing an integrated evolutionary computing method namely; SA with FPNM has been introduced to overcome such drawbacks. Three case studies based on the definition of objective functions and different error indices were used to evaluate the algorithm. The results showed that SA significantly reduced the sum of the total square deviations (SSQs) and the sum of absolute deviation (SAD) between the predicted and observed discharges compared to other evolutionary algorithms. Moreover, the proposed model achieved high ability to accurately determine the peak value and peak time of the discharge. In addition, the calculated hydrodynamic shape has a high correlation with observed hydrographs. [ABSTRACT FROM AUTHOR]

In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) have been used as successful algorithms in such engineering application for optimizations in order to introduce a comprehensive comparison analysis with the results of WOA. Two case studies were considered in this research. The aim of the first case study was to minimize irrigation water deficits for Aydoughmoush dam in Iran. Results showed that average solution of the operation rule of the WOA is close to the global solution and that reliability index and resiliency index for water supply, based on WOA, are higher than the GA and PSOA. Also, the operation rule achieved by the WOA has well-supplied water to match the water demands for the operation period (1991–2000). The second case study was related to minimize the electricity power deficits in Karun-4 reservoir. In fact, the water should be released such that the electricity power generation is maximized or the electricity power deficit should be minimized. Results showed that the operation rule for the water achieved by the WOA produces more electrical power than GA and PSOA. Furthermore, the average solution accomplished by WOA is very close to the global solution. Different indexes were used for evaluating the proposed methods, namely reliability index, vulnerability index, and resiliency index. For the first case study, the proposed WOA could achieve 98% reliability which is outperformed the other GA and PSOA methods. On the other hand, for the second case study, the proposed WOA method could attain electricity power 12 and 15% more than the one generated while using the GA and PSOA methods. Thus, WOA has high potential to be applied for achieving optimal operation rule for dam and reservoir operation and better water resources management and plan. [ABSTRACT FROM AUTHOR]

Water resources scarcity and competition among stakeholders in water allocation always highlights the optimal operation of water resources. This research examines the operation of a multi-purpose water reservoir aiming at providing agricultural, urban, industrial and environmental demands. A new evolutionary Hybrid Algorithm (integrating Bat Algorithm (BA) and Particle Swarm Algorithm (PSA)) first specified total monthly water release ​​to meet the total monthly needs (agricultural, urban, industrial and environmental) to operate the optimal reservoir. The new Hybrid Algorithm (HA) helps BA and PSA to increase the convergence rate and achieve an optimal absolute response by eliminating the weak responses of one algorithm and substituting the strong responses of the other algorithm. Released water was then allocated to different demands based on the proportional method in the Game Theory (GT). The optimal operation of Shahid Dam Reservoir in Fars, southern Iran is the case study of this research, which has been a serious challenge for decision makers to meet various water demands. The convergence rate of HA was more than that of BA and PSA based on the results. Moreover, the HA with volumetric reliability of 0.92, 0.89, 0.79 and 0.75 was used to meet the urban, environmental, agricultural and industrial demands; while, the volumetric reliability indices of BA and PSA were less than the HA. Also, the mean water release values by the HA met different demands much better than BA and PSA in a period of five years. [ABSTRACT FROM AUTHOR]

Concerning water and energy crisis, energy management and correct utilization of resources are important. In the present study, utilization of a multi-reservoir system was addressed with an approach to improve production of hydroelectric energy. For this purpose, Monarch Butterfly Algorithm (MBA), which is a new evolutionary algorithm, was used. Three periods of dry (1963–64), wet (1951–52) and normal (1985–86) conditions were considered in the operation of a 4-reservoir system. Results showed that MBA was capable of generating more energy as compared to Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). For instance, MBA improved the accuracy of GA and PSO in generating energy by 1.16 and 0.88 percent in the wet year, 1.28 and 1.2 percent in the dry year and 1.34 and 0.88 percent in the normal year, respectively. Moreover, quality of the responses obtained from the MBA was better than those of the other two algorithms, because coefficients of variation of the responses in MBA were less. [ABSTRACT FROM AUTHOR]

Abstract Optimization is an interesting method that finds the optimal response (i.e., objective function) by searching the decision variables in the search space. The present study deals with the optimal design of a labyrinth spillway (LS) having half-round or quarter-round crest shape. For optimal the design of the spillway geometry, an evolutionary Hybrid Algorithm (HA) of Bat Algorithm (BA) and Particle Swarm Optimization (PSO) algorithm was developed. By this procedure, the worst responses of one algorithm are replaced by the best responses of the other algorithm. This HA does not get trapped in local minima and has high convergence rate concerning the optimal absolute response. Results of using HA showed that LS with half-round crest requires less concrete, compared to quarter-round crest. Moreover, the discharge capacity for LS with half-round crest was more than the quarter-round crest. The HA significantly reduced the computational time for the optimal design of LS with half-round or quarter-round crest, as compared to BA or PSO. Highlights • New method for optimal design of spillway. • Comprehensive study for the optimal design of spillway. • The designing of spillways with the different crest shapes. [ABSTRACT FROM AUTHOR]