Modified BNMR algorithm applied to Loney's solenoid benchmark problem

Taherdangkoo, Mohammad; Taherdangkoo, Reza

doi:10.3233/jae-141965

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…Utilizing different linear and nature-inspired algorithms (like blind naked mole-rat (BNMR) and SIRT algorithms) enabled the scientists to better deal with various engineering issues [22,23]. Furthermore, many scholars have proven that the incorporation of hybrid metaheuristic algorithms with a regular predictive algorithm can result in computational drawbacks like local minima [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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Section: Introductionmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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“…(2) In order to improve the anti-sour property, the wellhead is made of reinforced concrete material. (3) In order to realize the initial split-flow control, the recharge wellhead is made of an impermeable body, within a certain range of the bottom, which can prevent wastewater from flowing into the well, at the initial stage of rainfall. (4) In order to prevent wastewater from recharging into the recharge well, when the river water is polluted, a geomembrane can be used to cover the outer layer of the geotextile, which serves as an impermeable layer.…”

Section: Design Of the Iaf Recharge-pumping Wellsmentioning

confidence: 99%

“…The coefficient of uniformity was 3.20, and the coefficient of curvature was 1.18, which was classified as non-performing coarse sand. The dry density of the sand was controlled in the range of 1.44-1.50 g/cm 3 , and the hydraulic conductivity was 3.07-3.55 × 10 −4 m/s. When the sand sample was filled, it was evenly scattered, loaded in layers, and compacted layer by layer.…”

Section: Test Equipmentmentioning

confidence: 99%

“…In areas where groundwater is the main source of water, water shortages and water quality problems have been caused by the severe over-exploitation of groundwater, which, in turn, has seriously hampered the social and economic development of those areas. Pingtan Island is a serious water-shortage area, in order to alleviate the shortage of water resources, people began to recharge abundant rain-flood resources and surface water into the aquifer by developing efficient recharge facilities [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Infiltration and Anti-Filtration Recharge-Pumping Well and Laboratory Recharge Tests

Jiang

et al. 2018

Water

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Infiltration and anti-filtration recharge-pumping wells (hereinafter, referred to as IAF recharge-pumping wells) can enable rain-flood flowing in rivers or channel recharge to aquifers, in flood periods, and pump groundwater to be utilized in non-flood periods. In this study, a round IAF recharge-pumping well and a square IAF recharge-pumping well were developed, the structure and characteristic were introduced, the calculation equations of single-well recharge quantity of IAF recharge-pumping wells, in unconfined aquifers were deduced, and the steady-state flow recharge test was conducted in the laboratory. The conclusions were as follows. The theoretical equation of the single-well recharge quantity was reasonable. Compared to existing anti-filtration recharge wells, the new IAF recharge-pumping well had stronger anti-deposit and anti-scour abilities and the single-well recharge quantity increased by 400%. Compared to the square IAF recharge-pumping well, the round IAF recharge-pumping well had a better inlet flow pattern and a larger single-well recharge quantity. With an increase in the test times, the single-well recharge quantity gradually decreased and tended to be stable. The existence of the pumping pipe had a little influence on the single-well recharge quantity.

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“…In summary, the theory and method of a nonlinear system applied to the prediction of water inrush from coal seam floor has been a hot topic in recent years [22,23]. The application of emerging model algorithms, such as dissipation, cooperation, mutation, chaos and fractals to water inrush prediction in coal mines, has become a new breakthrough in this field [24].In terms of coal seam floor water inrush prediction, a truly practical and effective prediction method has not yet been fully realized; there are many theoretical and practical problems that need…”

mentioning

confidence: 99%

A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

Duan

Cai

et al. 2018

Water

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Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields can provide adequate models for rock behavior on water inrush. In this study, a new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction. With the advantages of powerful global optimization functions, implicit parallelism and high stability of the GA, the penalty coefficient, insensitivity coefficient and kernel function parameter of the SVM model are determined as approximately optimal automatically in the spatial dimension. All of these characteristics greatly improve the accuracy and usable range of the SVM model. Testing results show that GA has a useful ability in finding optimal parameters of a SVM model. The performance of the GA optimized SVM (GA-SVM) is superior to the SVM model. The GA-SVM enables the prediction of water inrush and provides a promising solution to the predictive problem for relevant industries.

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Modified BNMR algorithm applied to Loney's solenoid benchmark problem

Cited by 6 publications

References 10 publications

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Infiltration and Anti-Filtration Recharge-Pumping Well and Laboratory Recharge Tests

A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

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