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

2019 ◽  
Vol 9 (22) ◽  
pp. 4738 ◽  
Author(s):  
Moayedi ◽  
Bui ◽  
Anastasios ◽  
Kalantar
Keyword(s):  
Shear Strength ◽  
Liquid Limit ◽  
Spotted Hyena ◽  
Key Factors ◽  
Ant Lion Optimization ◽  
Liquidity Index ◽  
Ant Lion ◽  
Artificial Neural Network Ann ◽  
Metaheuristic Techniques ◽  
Clay Percentage

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.

scholarly journals Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

2020 ◽  
Vol 10 (2) ◽  
pp. 689 ◽  
Author(s):  
Hossein Moayedi ◽  
Dieu Tien Bui ◽  
Phuong Thao Thi Ngo
Keyword(s):  
Shear Strength ◽  
Optimization Technique ◽  
Population Based ◽  
Liquid Limit ◽  
Coefficient Of Determination ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping ◽  
Training Error ◽  
Artificial Neural Network Ann ◽  
Metaheuristic Techniques

The prediction aptitude of an artificial neural network (ANN) is improved by incorporating two novel metaheuristic techniques, namely, the shuffled frog leaping algorithm (SFLA) and wind-driven optimization (WDO), for the purpose of soil shear strength (simply called shear strength) simulation. Soil information of the Trung Luong national expressway project (Vietnam) including depth of the sample (m), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content, wet density (kg/m3), liquid limit (%), plastic limit (%), plastic index (%), liquidity index, and the shear strength (kPa) was collocated through a field survey. After constructing the hybrid ensembles of SFLA–ANN and WDO–ANN, both models were optimized in terms of complexity using a population-based trial-and error-scheme. The learning quality of the ANN was compared with both improved versions to examine the effect of the used metaheuristic techniques. In this phase, the training error dropped by 14.25% and 28.25% by applying the SFLA and WDO, respectively. This reflects a significant improvement in pattern recognition ability of the ANN. The results of the testing data revealed 25.57% and 39.25% decreases in generalization (i.e., testing) error. Moreover, the correlation between the measured and predicted shear strengths (i.e., the coefficient of determination) rose from 0.82 to 0.89 and 0.92, which indicates the efficiency of both SFLA and WDO metaheuristic techniques in optimizing the ANN.

scholarly journals Artificial Neural Network (ANN) Model for Shear Strength of Soil Prediction

2021 ◽  
Vol 411 ◽  
pp. 157-168
Author(s):  
Jacqueline A. Richard ◽  
Norazzlina M. Sa’don ◽  
Abdul Razak Abdul Karim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Input Parameter ◽  
Random Errors ◽  
Ann Model ◽  
Predictive Tool ◽  
Liquidity Index ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Geotechnical structures, design of embankment, earth and rock fill dam, tunnels, and slope stability require further attention in determining the shear strength of soil and other parameters that govern the result. The shear strength of soil commonly obtained by conducting laboratory testing such as Unconfined Compression Strength (UCS) Test and Unconsolidated Undrained (UU) Test. However, random errors and systematic errors can occur during experimental works and caused the findings imprecise. Besides, the laboratory test also consuming a lot of time and some of them are quite costly. Therefore, soft computational tools are developed to improve the accuracy of the results and time effectively when compared to conventional method. In this study, Artificial Neural Network (ANN) was employed to develop a predictive model to correlate the moisture content (MC), liquid limit (LL), plastic limit (PL), and liquidity index (LI) of cohesive soil with the undrained shear strength of soil. A total of 10 databases was developed by using MATLAB 7.0 - matrix laboratory with 318 of UCS tests and 451 of UU tests which are collected from the verified site investigation (SI) report, respectively. All the SI reports collected were conducted in Sarawak, Malaysia. The datasets were split into ratio of 3:1:1 which is 60:20:20 (training: validation: testing) with one hidden layer and eight hidden neurons. The input parameter of Liquidity index (LI) has shown the highest R-value (regression coefficient) which are 0.926 and 0.904 for UCS and UU model, respectively. In addition, the predictive models were tested and compare with the predicted and observed cohesion obtained from the collected experimental results. In summary, the ANN has the feasibility to be used as a predictive tool in estimating the shear strength of the soil.

scholarly journals The undrained strength – liquidity index relationship

2014 ◽  
Vol 51 (9) ◽  
pp. 1073-1086 ◽  
Author(s):  
P.J. Vardanega ◽  
S.K. Haigh
Keyword(s):  
Shear Strength ◽  
Undrained Shear Strength ◽  
Liquid Limit ◽  
Mathematical Function ◽  
Plastic Limit ◽  
Mathematical Relationship ◽  
Liquidity Index ◽  
Undrained Strength ◽  
Statistical Improvement ◽  
Undrained Shear

A database of 641 fall cone tests on 101 soil samples from 12 countries has been analysed to determine the best mathematical relationship linking undrained shear strength with liquidity index. From the database, it is shown that the use of a linear relationship linking liquidity index and the logarithm of undrained shear strength that uses the commonly assumed 100-fold factor increase in strength from the liquid to plastic limit overpredicts the measured data of soil strength. The use of a factor of about 35 for the ratio between the strength at liquid limit and that extrapolated to the plastic limit is shown to be more realistic. Logarithmic liquidity index is examined and found to also correlate strongly with the logarithm of undrained shear strength; however, it is shown that no great statistical improvement is present compared with the semi-logarithmic formulation. When considering data of individual soils a power law fitting is statistically shown to be the preferred mathematical function.

Test scheduling of System-on-Chip using Dragonfly and Ant Lion optimization algorithms

2020 ◽  
pp. 1-13
Author(s):  
Gokul Chandrasekaran ◽  
P.R. Karthikeyan ◽  
Neelam Sanjeev Kumar ◽  
Vanchinathan Kumarasamy
Keyword(s):  
Ant Colony Optimization ◽  
Artificial Bee Colony ◽  
Optimization Algorithms ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
On Chip

Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.

scholarly journals Efficient Energy Utilization Using Ant Lion Optimization in WSN

2021 ◽  
Vol 1717 ◽  
pp. 012034
Author(s):  
N. Padmapriya ◽  
S. Vinothini ◽  
S. Yuvasri
Keyword(s):  
Energy Utilization ◽  
Efficient Energy ◽  
Ant Lion Optimization ◽  
Ant Lion

scholarly journals Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

2021 ◽  
Vol 13 (6) ◽  
pp. 3198
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Residential Buildings ◽  
Optimization Technique ◽  
Hybrid Algorithms ◽  
Evolutionary Strategy ◽  
Multi Layer Perceptron ◽  
Invasive Weed ◽  
Dragonfly Algorithm ◽  
Ant Lion Optimization ◽  
Heating Load ◽  
Ant Lion

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system

2021 ◽  
Vol 63 (5) ◽  
pp. 442-447
Author(s):  
Hammoudi Abderazek ◽  
Ferhat Hamza ◽  
Ali Riza Yildiz ◽  
Liang Gao ◽  
Sadiq M. Sait
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Spur Gear ◽  
Ant Lion Optimization ◽  
Lion Algorithm ◽  
Weight Design ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Teaching Learning ◽  
Lion Optimization Algorithm

Abstract Metaheuristic optimization algorithms have gained relevance and have effectively been investigated for solving complex real design problems in diverse fields of science and engineering. In this paper, a recent meta-heuristic approach inspired by human social concepts, namely the queuing search algorithm (QSA), is implemented for the first time to optimize the main parameters of the spur gear, in particular, to minimize the weight of a single-stage spur gear. The effectiveness of the algorithm introduced is examined in two steps. First, the algorithm used is compared with descriptions in previous studies and indicates that the final results obtained by QSA lead to a reduction in gear weight by 7.5 %. Furthermore, the outcomes obtained are compared with those for the other five algorithms. The results reveal that the QSA outperforms the techniques with which it is compared such as the sine-cosine optimization algorithm, the ant lion optimization algorithm, the interior search algorithm, the teaching-learning-based algorithm, and the jaya algorithm in terms of robustness, success rate, and convergence capability.

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