Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron
Abstract
:1. Introduction
2. Background of the Used Hybrid Science
2.1. Shuffled Frog Leaping Algorithm
2.2. Wind-Driven Optimization
3. Study Area and Data Collocation
4. Shear Strength Prediction Using Neural Hybrids of SFLA–ANN and WDO–ANN
4.1. Data Division and Preprocessing
4.2. Initializing the MLP for Representing the ANN
4.3. Defining Modeling Parameters
4.4. Hybridizing the ANN Using the SFLA and WDO
4.5. Accuracy Assessment Criteria
5. Results and Discussion
6. Concluding Remarks
Author Contributions
Funding
Conflicts of Interest
References
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Features | Descriptive Index | |||||||
---|---|---|---|---|---|---|---|---|
Mean | SE * | Median | SD ** | SV *** | Skewness | Minimum | Maximum | |
Depth of sample (m) | 16.21 | 0.71 | 13.00 | 12.57 | 157.94 | 1.02 | 1.00 | 52.00 |
Sand (%) | 8.40 | 0.73 | 2.40 | 13.04 | 169.92 | 1.85 | 0.00 | 57.30 |
Silt (%) | 54.01 | 0.70 | 56.10 | 12.46 | 155.30 | −0.30 | 20.00 | 83.70 |
Clay (%) | 37.28 | 0.66 | 37.60 | 11.68 | 136.35 | 0.01 | 10.40 | 63.90 |
Moisture content (%) | 48.47 | 1.35 | 49.90 | 23.90 | 571.41 | 0.12 | 17.10 | 90.30 |
Wet density (kg/m3) | 1767.68 | 12.09 | 1690.00 | 214.60 | 46,054.17 | 0.09 | 1470.00 | 2150.00 |
Liquid limit (%) | 54.47 | 0.84 | 54.90 | 14.89 | 221.65 | −0.23 | 21.40 | 79.90 |
Plastic limit (%) | 24.72 | 0.30 | 24.40 | 5.39 | 29.05 | 0.02 | 12.90 | 35.90 |
Plastic Index (%) | 29.75 | 0.56 | 30.40 | 10.00 | 99.92 | −0.27 | 6.60 | 49.70 |
Liquidity index | 0.70 | 0.03 | 0.79 | 0.46 | 0.22 | 0.04 | 0.01 | 1.73 |
Shear strength (kPa) | 42.12 | 1.76 | 20.81 | 31.31 | 980.23 | 0.55 | 8.27 | 130.00 |
Type of Data | Observed Value (kPa) | Model Results | Relative Error (%) | ||
---|---|---|---|---|---|
SFLA–ANN | WDO–ANN | SFLA–ANN | WDO–ANN | ||
Minimum | 8.27 | 12.55 | 13.01 | 51.79 | 57.33 |
9.34 | 16.69 | 14.13 | 78.76 | 51.33 | |
9.34 | 15.92 | 14.88 | 70.40 | 59.26 | |
10.27 | 12.53 | 12.34 | 22.12 | 20.19 | |
10.27 | 15.29 | 11.51 | 48.96 | 12.15 | |
Maximum | 110.63 | 78.63 | 86.41 | −28.93 | −21.89 |
111.55 | 72.08 | 72.78 | −35.39 | −34.76 | |
113.76 | 79.04 | 86.82 | −30.51 | −23.68 | |
114.25 | 77.97 | 79.65 | −31.76 | −30.28 | |
130.01 | 78.88 | 82.44 | −39.32 | −36.59 |
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Moayedi, H.; Bui, D.T.; Thi Ngo, P.T. Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron. Appl. Sci. 2020, 10, 689. https://doi.org/10.3390/app10020689
Moayedi H, Bui DT, Thi Ngo PT. Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron. Applied Sciences. 2020; 10(2):689. https://doi.org/10.3390/app10020689
Chicago/Turabian StyleMoayedi, Hossein, Dieu Tien Bui, and Phuong Thao Thi Ngo. 2020. "Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron" Applied Sciences 10, no. 2: 689. https://doi.org/10.3390/app10020689
APA StyleMoayedi, H., Bui, D. T., & Thi Ngo, P. T. (2020). Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron. Applied Sciences, 10(2), 689. https://doi.org/10.3390/app10020689