Comparison of meta-heuristic algorithms in optimum operation of a single-reservoir dam system

Donyaii, Alireza; SarrafAmirpouya,; Ahmadi, Hassan

doi:10.1680/jensu.20.00065

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…It was found that HHO was among the top performing models in the study in terms of objective value function, CPU run-time, and convergence rate. The usage of HHO in dam reservoir operation optimization has also been found in studies by Donyaii et al (2021) and Nguyen et al (2021). Although studies on WOA, LFWOA, and HHO in dam reservoir operation optimization are currently limited, these algorithms have been demonstrated to produce good performances in other fields.…”

Section: Introductionmentioning

confidence: 85%

Investigating dam reservoir operation optimization using metaheuristic algorithms

et al. 2022

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The optimization of dam reservoir operations is of the utmost importance, as operators strive to maximize revenue while minimizing expenses, risks, and deficiencies. Metaheuristics have recently been investigated extensively by researchers in the management of dam reservoirs. But the animal-concept-based metaheuristic algorithm with Lévy flight integration approach has not been used at Karun-4. This paper investigates the optimization of dam reservoir operation using three unexplored metaheuristics: the whale optimization algorithm (WOA), the Levy-flight WOA (LFWOA), and the Harris hawks optimization algorithm (HHO). Utilizing a time series data set on the hydrological and climatic characteristics of the Karun-4 hydroelectric reservoir in Iran, an analysis was conducted. The objective functions and constraints of the Karun-4 hydropower reservoir were examined throughout the optimization procedure. HHO produces the best optimal value, the least-worst optimal value, the best average optimal value, and the best standard deviation (SD) with scores of 0.000026, 0.001735, 0.000520, and 0.000614, respectively, resulting in the best overall ranking mean (RM) with a score of 1.5 at Karun-4. Throughout the duration of the test, the optimized trends of water release and water storage indicate that HHO is superior to the other investigated metaheuristics. WOA has the best correlation of variation (CV) with a score of 0.090195, while LFWOA has the best convergence rate (3.208 s) and best CPU time. Overall, it can be concluded that HHO has the most desirable performance in terms of optimization. Yet, current studies indicate that both WOA and LFWOA generate positive and comparable outcomes.

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Section: Introductionmentioning

confidence: 85%

Investigating dam reservoir operation optimization using metaheuristic algorithms

et al. 2022

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“…e vertical range of the aquifer can be further disrupted by variations in hydraulic conductivity (hydraulic conductivity multiplied by saturated aquifer thickness). Hydraulic characteristics or other elements that have a significant positive or negative influence on groundwater modelling are of higher importance in the modelling [8]. e specific yield ranges between 0.02 and 0.2.…”

Section: Optimizing Parameters Using the Hho Algorithmmentioning

confidence: 99%

“…To consider the multiobjective existence of groundwater systems, one approach is to specify multiple optimization objective functions that quantify different characteristics of system action. ey employ a multiobjective optimization tool to find a collection of nondominated solutions named as Pareto best approaches [8,9]. e Pareto solutions reflect trade-offs among various incomputable and often competing goals, with the property that switching from one solution to another improves one while deteriorating one or more others [10].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Single‐ and Multiobjective Optimization Using Harris Hawks and Multiobjective Billiards‐Inspired Algorithm

Gerey

Sarraf

Ahmadi

2021

Shock and Vibration

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This is the first attempt to combine the Multiobjective Billiards-Inspired Optimization Algorithm (MOBOA) with groundwater modelling to determine pumping rates within a well-distributed range of Pareto options. In this study, in order to determine an optimum solution for groundwater drawdown, pumping rates were selected accompanied by three minimization objectives: minimizing shortage influenced by inability to supply, adjusted shortage index, and minimizing the degree of drawdown within predefined areas. To optimize hydraulic conductivity and specific yield parameters of a modular three-dimensional finite-difference (MODFLOW) groundwater model, the Harris Hawks optimization algorithm was used to minimize the sum of absolute deviation between observed and simulated water-table levels. MOBOA was then utilized to optimize pumping rate variables for an Iranian arid to semiarid groundwater environment using these parameters. As the study results, when the maximum and minimum aquifer drawdown was specified in the range of −40 to +40 cm/year, the Pareto parameter sets produced satisfactory results. Overall, the “Simulation-Optimization-Modelling” protocol was able to generate a series of optimal solutions that were shown on a Pareto front. The study concluded to an optimum approach that provides policy makers in the Iranian water stressed zones with safe groundwater management alternatives.

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“…Then, the best-worst fuzzy multi-criteria decision model in Lingo9 software was developed by collecting the opinions of 23 GRP experts, based on three general criteria of change in administrative structure (C1), policy structure (C2) and modification of GRP legal structure (C3), which includes 18 sub-criteria. Donyaii et al (2021) evaluated and compared the performance of the farmland fertility algorithm (FFA) and that of the Harris hawks optimization (HHO) algorithm with each other to solve the two-objective problem of optimal operation of the Golestan dam reservoir under baseline (April 2006-October 2018 and climate change conditions (April 2021-October 2033) in Golestan Province, Iran and the results showed that when considering a constant value of the reliability index (i.e. 80%), the rate of vulnerability changes in the multi-objective FFA (Moffa) is lower than that of the multi-objective HHO (Mohho) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Calibration of SWAT and three data-driven models for monthly stream flow simulation in Maharlu Lake Basin

2021

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In this study, the performance of SWAT hydrological model and three computational intelligence methods used to simulate river flow are investigated. After collecting the data required for all models used, the calibration and validation stages were performed. Using the SWAT model and three methods of the Extreme Machine Learning (EML), the Support Vector Regression (SVR), and the Least Squares Support Vector Regression (LSSVR), Maharlu Lake Basin stream flow was simulated and the results obtained at Shiraz station were used for this study. A noise reduction filter was employed to improve the results from the computational intelligence methods, and SUFI-2 algorithm was used to analyze the uncertainty of the SWAT model. Finally, in order to evaluate the models developed and the SWAT model, three statistics (RMSE), (R²), and (NS) coefficient were used. The results indicated that the SWAT model and the machine learning models were generally appropriate tools for daily flow modeling, but the LSSVR model showed less errors in both learning and testing, with the coefficients NS = 0.997 and R² = 0.997 in the calibration stage and NS = 0.994 and R² = 0.994 in the validation stage, which prove their better performance compared to the other methods and the SWAT model.

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Comparison of meta-heuristic algorithms in optimum operation of a single-reservoir dam system

Cited by 5 publications

References 14 publications

Investigating dam reservoir operation optimization using metaheuristic algorithms

Investigating dam reservoir operation optimization using metaheuristic algorithms

Groundwater Single‐ and Multiobjective Optimization Using Harris Hawks and Multiobjective Billiards‐Inspired Algorithm

Calibration of SWAT and three data-driven models for monthly stream flow simulation in Maharlu Lake Basin

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