A novel Master–Slave optimization algorithm for generating an optimal release policy in case of reservoir operation

Turgut, Mert Sinan; Turgut, Oğuz Emrah; Afan, Haitham Abdulmohsin; El-Shafie, Ahmed

doi:10.1016/j.jhydrol.2019.123959

Cited by 20 publications

(13 citation statements)

References 39 publications

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“…HB-SA outperformed the other algorithms, with the highest reliability of 94% and 96% for the Golestan Dam and Voshmgir Dam, respectively. However, the same suggestion made in [ 21 ] has also been mentioned in this study, namely that future studies should take into account the lack of suitable climate conditions and the effect of climate change on reservoir operations.…”

Section: Review Of Swarm Intelligence (Si) For Optimising Reservoir O...supporting

confidence: 68%

“…However, it can be concluded that the future recommendations of this review should be more focused and concerned with investigating the hydrological variables with climate change impacts on future reservoir operations under different climate scenarios, in accordance with global climate change strategies. As highlighted in the past studies [ 21 , 47 ], the uncertainties of climate-change or scenarios may greatly influence the reservoir operation in future reservoir policy. Hence, this would allow for more practical and reliable for obtaining optimal reservoir operation rules into the future.…”

Section: Discussionmentioning

confidence: 99%

“…There are a few metrics for the efficacy of algorithms to be verified and evaluated [ 21 ]. The terms reliability, resilience, vulnerability, and sustainability index are frequently used to describe how reservoir system policies perform (RSP) [ 22 ].…”

Section: Reservoir Operation Optimisation Innovation and Techniquesmentioning

confidence: 99%

“…In Scenario 2, however, forecasted deterministic variables were used to optimise present unrealistic conditions in providing the best reservoir policy to minimise the water deficit. Aside from that, an ensembled technique called the Master–slave model approach, which is made up of PSO, DE, Whale Optimisation algorithm, and Crow Search algorithm [ 21 ] was followed. This approach gathered the domain information processed by each population in the respective algorithms, exchanged and evaluated the data based on the algorithms, determined the global optimum solution, and finally formed the Master–slave approach for generating the optimal reservoir policy at AHD.…”

Section: Review Of Swarm Intelligence (Si) For Optimising Reservoir O...mentioning

confidence: 99%

See 3 more Smart Citations

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Lai

Huang

Koo

et al. 2022

Arch Computat Methods Eng

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Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.

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Section: Review Of Swarm Intelligence (Si) For Optimising Reservoir O...supporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Reservoir Operation Optimisation Innovation and Techniquesmentioning

confidence: 99%

Section: Review Of Swarm Intelligence (Si) For Optimising Reservoir O...mentioning

confidence: 99%

See 2 more Smart Citations

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Lai

Huang

Koo

et al. 2022

Arch Computat Methods Eng

Self Cite

View full text Add to dashboard Cite

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“…However, with the demographic explosion, the industrial growth and the various forms of life in several areas worldwide, water requirements have considerably increased; this has caused a mismatch between water demand and water supply; therefore, the problems of water availability became amplified and water resources become under high pressure. As a result, appropriate management of these resources becomes a major concern in order to minimize this pressure or to bridge the hiatus between water availability and demand [1]- [3]. In order to be eligible to arrange some water management tactics to make it obtainable anytime, a prediction of the interrelationship between the two main components of the hydrological cycle rainfall and runoff is necessary [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms

et al. 2021

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Developing trustworthy rainfall-runoff (R-R) models can offer serviceable information for planning and managing water resources. Use of artificial neural network (ANN) in adopting such models and predicting changes in runoff has become popular among many hydrologists from a long time. However, since the optimization is the most significant phase in ANN training, researchers' attentiveness has been attracted to the ANN's biggest problem, i.e. its susceptibility of being blocked in local minima. Consequently, use of genetic algorithms (GA), particle swarm optimization (PSO), firefly algorithm (FFA) and improved particle swarm optimization (IPSO) approaches to increase the performance of ANN, have gained remarkable interest among distinct modern heuristic optimization approaches. In this paper, the capability of four improved ANN methods, hybrid GA-based ANN, PSO-based ANN, FFA-based ANN and IPSO-based ANN in modeling rainfall-runoff (R-R) is investigated. IPSO has been used in order to increase the ability of PSO, where the new positions of particles are dynamically adjusted using two procedures which is given form the velocity obtained by PSO and proposed velocity in IPSO. The random normal grated number with a dynamical scale factor is used to compute the new position of the best particles in proposed velocity. Daily R-R data from six stations distributed in the Seybouse watershed located in semi-arid region in Algeria were used in models' development. The selection of the input data sets was carried out using the autocorrelation, partial autocorrelation and cross correlation functions. The results of the four hybrid models were compared via performance metrics, viz., Root Mean Square Error (RMSE), Pearson's correlation coefficient (R), Nash Sutcliffe Efficiency coefficient (NSE), and via graphical analysis (scatter plots, time series and Taylor diagram). Outcomes of the analysis at all study stations disclosed that all the ANN models enhanced with IPSO overachieved the GA-based ANN, PSObased ANN and FFA-based ANN models in estimating runoff for both training and testing periods. The outcomes of the study indicate that the IPSO hybrid metaheuristic algorithm is the best technique in improving ANN capability in modeling daily R-R.

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A novel structural reliability analysis method combining the improved beluga whale optimization and the arctangent function‐based maximum entropy method

Wang,

Li,

Zhang

et al. 2024

Quality & Reliability Eng

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A novel structural reliability analysis method that combines the improved beluga whale optimization (IBWO) and the arctangent function‐based maximum entropy method (AMEM) is proposed in this paper. It aims to augment the accuracy of failure probability prediction in structural reliability analysis based on the traditional maximum entropy method (MEM). First, the arctangent function is introduced to avoid the effects of truncation error and numerical overflow in the traditional MEM. The arctangent function can nonlinearly transform the structural performance function defined on the infinite interval into a transformed performance function defined on the bounded interval. Subsequently, the undetermined Lagrange multipliers in the maximum entropy probability density function (MEPDF) of the transformed performance function are obtained using IBWO at a swifter convergence speed with heightened convergence accuracy. Finally, the MEPDF of the transformed performance function can be obtained by combining the IBWO and AMEM, and the structural failure probability can be predicted. The analysis of the metro bogie frame as an engineering example reveals that compared with the traditional MEM using the genetic algorithm to solve the Lagrange multipliers, the proposed method diminishes the relative error in failure probability prediction from 20.51% to only 0.09%. This method significantly enhances the prediction accuracy of failure probability.

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A novel Master–Slave optimization algorithm for generating an optimal release policy in case of reservoir operation

Cited by 20 publications

References 39 publications

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms

A novel structural reliability analysis method combining the improved beluga whale optimization and the arctangent function‐based maximum entropy method

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