A Systematic Review of Optimization of Dams Reservoir Operation Using the Meta-heuristic Algorithms

Beiranvand, Behrang; Ashofteh, Parisa‐Sadat

doi:10.1007/s11269-023-03510-3

Cited by 11 publications

(5 citation statements)

References 129 publications

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“…Therefore, this paper adopts an enhanced hunting 'failure' formula to enhance the ability of the algorithm to jump out of the local optimum. The enhanced hunting 'failure' formula is shown in Equation (8).…”

Section: Improvement Of Hunting 'Failure'mentioning

confidence: 99%

“…The extensive application of meta-heuristic algorithms in intricate scientific and engineering challenges is attributed to their broad applicability and high computational efficiency, and the ability to scale problems [4][5][6]. Group intelligence optimization algorithms, influenced by the smart actions of biological groups, have garnered interest from numerous academics [7][8][9]. For example, Pigeon-inspired Optimization (PIO) was proposed based on the autonomous homing behavior of domestic pigeons [10]; Beluga Whale Optimization (BWO) was inspired by the daily life behaviors of beluga whales [11]; the Crayfish Optimization algorithm (COA) simulates crayfish summering, competitive, and foraging behaviors [12]; the Whale Optimization Algorithm (WOA) is based on the humpback whale's bubble net hunting strategy [13]; the Butterfly Optimization Algorithm Mathematics 2024, 12, 1459 2 of 33 (BOA) is influenced by the natural behavior of butterfly predation [14]; the optimization algorithm (GJO) for golden jackals mimics their cooperative hunting behavior [15], and that for sand cat swarms mimics the predatory behavior of sand cats (SCSO) in nature [16].…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

Zhang,

Shi,

Wang

et al. 2024

Mathematics

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Aiming at the problems of the Tyrannosaurus optimization algorithm, of poor search accuracy, insufficient global search capability, and ease of falling into local optimality, a dynamic hierarchical improved Tyrannosaurus optimization algorithm (DHTROA) with hybrid topology structure is proposed. Initially, a chaotic opposition-based learning approach is selected to start the population, ensuring a more uniform distribution of prey across the solution area and boosting population diversity; later, a dynamic hybrid bi-population strategy is introduced to divide the initial population into an ‘advantaged group’ and a ‘disadvantaged group’ to improve the efficiency of individual information exchange. Finally, the ‘advantaged group’ and ‘disadvantaged group’ are hunted synchronously; for the ‘advantaged group’, the position update is carried out using the cellular ring topology strategy, and for the ‘disadvantaged group’, the original algorithm is run in accordance with the main loop process. For the problem of the constant running rate of the Tyrannosaurus in the original algorithm, an adaptive running rate strategy is proposed, which enhances the ability of global optimization, and at the same time, the shortcomings of the original algorithm’s ‘failure’ strategy are improved in order to enhance the original algorithm to jump out of extrema. DHTROA was tested for performance with nine optimization algorithms in different dimensions of the CEC2017 test function. The efficiency of these enhancements was confirmed through the Wilcoxon rank sum test and Friedman test, while DHTROA was utilized for six engineering optimization challenges of differing complexities. The experimental results show that DHTROA has improved greatly in convergence speed, optimality search accuracy, global search ability, and stability, and the excellent engineering optimization performance also proves the excellent robustness of DHTROA.

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Section: Improvement Of Hunting 'Failure'mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

Zhang,

Shi,

Wang

et al. 2024

Mathematics

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“…As such, EC-based algorithms, using their unique natural structure, can address the complexities of reservoir operation problems such as discontinuity, uncertainty, nonlinearity, multi-objectives, and discreteness. For more information about using metaheuristic algorithms in dam operation, see reviews [78][79][80].…”

Section: Topic 1: Optimization Modelsmentioning

confidence: 99%

Dam System and Reservoir Operational Safety: A Meta-Research

Badr,

Li,

El-Dakhakhni

2023

Water

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Dams are critical infrastructure necessary for water security, agriculture, flood risk management, river navigation, and clean energy generation. However, these multiple, and often conflicting, objectives introduce complexity in managing dam operations. In addition, dam infrastructure has been evolving as complex systems-of-systems with multiple interacting components and subsystems, all susceptible to a wide range of uncertainties. Such complexities and uncertainties have triggered extensive research initiatives focused on dam systems and reservoir operational safety. Focusing on the latter, this paper meta-researches (conducts research-on-research) previously published studies to identify the critical research gaps and propose future research directions. In this respect, this paper first performs a quantitative analysis of the pertinent literature, using text mining and subsequent topic modeling, to identify and classify major and uncover latent topics in the field. Subsequently, qualitative analysis is conducted to critically review the identified topics, exploring the concepts, definitions, modeling tools, and major research trends. Specifically, the study identified seven topics: optimization models; climate change; flood risk; inflow forecasting; hydropower generation; water supply management; and risk-based assessment and management. The study also presents three main research gaps associated with the limitations in modeling concepts, modeling tools capabilities, and the lack of resilience-guided management of dam operational safety. Overall, this study presents a road map of the currently available dam and reservoir operational safety research and associated knowledge gaps, as well as potential future research directions to ensure the resilience of such critically important infrastructure, especially in the age of climate change.

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“…Our state space is deĄned by the level of the reservoirs, which is conĄned within the hyperrectangle S t := {s t ∈ IR n | s ≤ s t ≤ s}, as deĄned by the box constraint (5). As a result, the state space is continuous, and as aforementioned, the approximate value function ( 10)-( 16) cannot be evaluated for all possible pairs (s t , q t ).…”

Section: Simplicial Sampling Of the Reservoir Level Spacementioning

confidence: 99%

“…Numerous meta-heuristic approaches have also been proposed for reservoir optimization problems, e.g., [3]. Two recent systematic reviews of such methods are available in [4,5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

Zephyr,

Lamond,

Lang

2023

Preprint

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We revisit an approximate stochastic dynamic programming method that we proposed earlier for the optimization of multireservoir problems. The method exploits the convexity properties of the value function to sample the reservoir level space based on the local curvature of the value function, which is estimated by the difference between a lower and an upper bounds (error bound). Unlike the previous approach where the state space was exhaustively partitioned into full dimensional simplices whose vertices formed a discrete grid over which the value function was approximated, here we propose instead a new randomized approach for selecting the grid points from a small number of randomly sampled simplices from which an error bound is estimated. Results of numerical experiments on three literature test problems and simulated midterm reservoir optimization problems illustrate the advantages of the randomized approach which can solve models of higher dimensions than with the exhausitive approach.

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A Systematic Review of Optimization of Dams Reservoir Operation Using the Meta-heuristic Algorithms

Cited by 11 publications

References 129 publications

A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

Dam System and Reservoir Operational Safety: A Meta-Research

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

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