A comprehensive review on water cycle algorithm and its applications

Nasir, Mohammad; Sadollah, Ali; Choi, Young Hwan; Kim, Joong Hoon

doi:10.1007/s00521-020-05112-1

Cited by 49 publications

(12 citation statements)

References 208 publications

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“…The position vector specifies the variable values for the problem under study. For example, M 32 = [5,10,7,9] means that the problem is of 4 variables and whose values, for monkeypox copy number 3 at generation 2, are 5, 10, 7 and 9, respectively. At first (generation j = 1), the virus searches for the desired cell and then performs the rest of the steps.…”

Section: Monkeypox Optimization (Mo) Algorithmmentioning

confidence: 99%

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A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

Mohamed

Hamed

2023

Preprint

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Section: Monkeypox Optimization (Mo) Algorithmmentioning

confidence: 99%

“…One more direction is to mimic the natural physical phenomena involving the physics of matter and energy. Examples in this direction are water cycle algorithm [7] and central force optimization [8].…”

Section: Introductionmentioning

confidence: 99%

A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

Mohamed

Hamed

2023

Preprint

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“…This result is mainly due to the unsatisfactory balance between exploitation and exploration, which leads to trapping at local optima and premature convergence. In fact, the exploration phase may not play a role in determining the final solution (Xu and Mei 2018;Nasir et al 2020), which increases the burden of exploration.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Xia

Guo

et al. 2022

Landslides

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Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and water cycle algorithm (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), metaheuristic support vector regression (SVR), and the nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, and convergence. The results obtained for the Shuping and Baishuihe landslides demonstrate that the hybrid approach can be utilized to determine the optimum hyperparameters and present statistical significance, thus enhancing accuracy and reliability in ML-based prediction. Significant differences were observed among the five metaheuristics. Based on the Friedman test, which was performed on the root mean square error (RMSE), Kling-Gupta efficiency (KGE), and computational time, PSO is recommended for hyperparameter tuning for SVR-based displacement prediction due to its ability to maintain a balance between precision, computational time, and robustness. The nonparametric Friedman test is promising for presenting statistical significance, thus enhancing reproducibility.

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“…In contrast, the individuals (stream) move indirectly to the optimal solution (ocean) because of the three-level architecture (stream, river, and sea) of the WCA, which can avoid falling into the local optimal solution. At the same time, in the search process, the individuals (streams) are guided to move towards better positions (rivers) to avoid searching inappropriate areas, which is conducive to the search efficiency of the WCA [40,41].…”

Section: Establishment Of Lhs-wca Algorithmmentioning

confidence: 99%

Calibration of Adjustment Coefficient of the Viscous Boundary in Particle Discrete Element Method Based on Water Cycle Algorithm

Gao²,

Cheng

et al. 2022

Water

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The viscous boundary has a direct influence on the accuracy of structural dynamic response analysis, and the absorbing effect of the viscous boundary is controlled by the adjustment coefficient. Therefore, a calibration model of the viscous boundary’s adjustment coefficient based on the water cycle algorithm is established for the particle discrete element to improve the accuracy of dynamic response analysis. First, the traditional viscous boundary theory is utilized to realize the viscous boundary’s application method in the particle discrete element via programming. This avoids the reflection and superposition of seismic waves at the boundary and makes the structural dynamic response with the particle discrete element more real and accurate. Second, for the complex and time-consuming adjustment coefficients determination, a calibration model based on the water cycle algorithm and Latin hypercube sampling is established for the adjustment coefficients in the particle discrete element method. Finally, this calibration model is employed for the seismic response analysis of a rockfill slope, the maximum velocity of rock in this rockfill slope being about 1.30 times that of a seismic wave. Comparing the rockfill slope response with fixed and viscous boundaries, the calibration’s accuracy and the viscous boundary’s feasibility are demonstrated, further expanding the research and application of the particle discrete element method in dynamic response analysis.

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A comprehensive review on water cycle algorithm and its applications

Cited by 49 publications

References 208 publications

A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

A New Promising Evolutionary Optimization Algorithm Inspired by Monkeypox Replication Lifecycle

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Calibration of Adjustment Coefficient of the Viscous Boundary in Particle Discrete Element Method Based on Water Cycle Algorithm

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