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 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.

Multi-objective parameter optimization of the HYPE model using shuffled frog-leaping algorithm

2021 ◽  
Author(s):  
Xinyu Li ◽  
Prajna Kasargodu Anebgailu ◽  
Jörg Dietrich
Keyword(s):  
Optimization Problems ◽  
Population Based ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Hydrological Models ◽  
Watershed Model ◽  
Shuffled Frog Leaping Algorithm ◽  
Multi Objective ◽  
Combinatorial Optimization Problems ◽  
Shuffled Frog Leaping

<p>The calibration of hydrological models using bio-inspired meta-heuristic optimization techniques has been extensively tested to find the optimal parameters for hydrological models. Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. The frogs search locally in each memeplex and are periodically shuffled into new memeplexes to ensure global exploration. Though it is developed for discrete optimization, it can be used to solve multi-objective combinatorial optimization problems as well.</p><p>In this study, a hydrological catchment model, Hydrological Predictions for the Environment (HYPE) is calibrated for streamflow and nitrate concentration in the catchment using SFLA. HYPE is a semi-distributed watershed model that simulates runoff and other hydrological processes based on physical as well as conceptual laws. SFLA with 200 runtimes and 5 memeplexes containing 10 frogs each is used to calibrate 22 model parameters. It is compared with manual calibration and Differential Evolution Markov Chain (DEMC) method from the HYPE-tool. The preliminary results of the statistical performance measures for streamflow calibration show that SFLA has the fastest convergence speed and higher stability when compared with the DEMC method. NSE of 0.68 and PBIAS of 7.72 are recorded for the best run of SFLA during the calibration of streamflow. In comparison, the HYPE-tool DEMC produced the best NSE of 0.45 and a PBIAS of -3.37 while the manual calibration resulted in NSE of 0.64 and PBIAS of 2.01.</p>

scholarly journals REDUCTION OF REAL POWER LOSS BY IMPROVED SHUFFLED FROG-LEAPING ALGORITHM

2018 ◽  
Vol 6 (10) ◽  
pp. 146-157
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Optimization Technique ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Real Power ◽  
Shuffled Complex Evolution ◽  
Particle Swarm Optimization Technique ◽  
Shuffled Frog Leaping ◽  
Power Problem

This paper presents Improved Shuffled Frog-Leaping (ISFL) algorithm for solving optimal reactive power problem. A new search-acceleration parameter has been introduced into the formulation of the original shuffled frog leaping (SFL) algorithm to create an adapted form of the shuffled frog algorithm for solving the reactive power problem. The shuffled frog-leaping algorithm draws its formulation from two other search techniques: the local search of the ‘particle swarm optimization’ technique; and the competitiveness mixing of information of the ‘shuffled complex evolution’ technique. Proposed Improved Shuffled Frog-Leaping (ISFL) algorithm has been tested in standard IEEE 30,57,118 & Practical 191 Utility (Indian) System bus test systems and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss & control variables within the limits

scholarly journals An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring

2021 ◽  
Vol 46 (1) ◽  
pp. 27-42
Author(s):  
Alireza Goli ◽  
Erfan Babaee Tirkolaee ◽  
Gerhard-Wilhelm Weber
Keyword(s):  
Neural Network ◽  
Cnc Machining ◽  
Numerical Control ◽  
Shuffled Frog Leaping Algorithm ◽  
Consumption Data ◽  
Cnc Lathe ◽  
Shuffled Frog Leaping ◽  
Artificial Neural Network Ann ◽  
Machining Operations ◽  
Hybrid Methodology

Abstract This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).

scholarly journals The Hybrid Invasive Weed Optimization-Shuffled Frog-leaping Algorithm Applied to Optimal Design of Frame Structures

2019 ◽  
Author(s):  
Ali Kaveh ◽  
Siamak Talatahari ◽  
Nima Khodadadi
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Optimization Method ◽  
Population Based ◽  
Frame Structures ◽  
Invasive Weed Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Invasive Weed ◽  
Shuffled Frog Leaping

In this article, an efficient hybrid optimization algorithm based on invasive weed optimization algorithm and shuffled frog-leaping algorithm is utilized for optimum design of skeletal frame structures. The shuffled frog-leaping algorithm is a population-based cooperative search metaphor inspired by natural memetic, and the invasive weed optimization algorithm is an optimization method based on dynamic growth of weeds colony. In the proposed algorithm, shuffled frog-leaping algorithm works to find optimal solution region rapidly, and invasive weed optimization performs the global search. Different benchmark frame structures are optimized using the new hybrid algorithm. Three design examples are tested using the new method. This algorithm converges to better or at least the same solutions compared the utilized methods with a smaller number of analyses. The outcomes are compared to those obtained previously using other recently developed meta-heuristic optimization methods.

scholarly journals Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials

2019 ◽  
Vol 9 (8) ◽  
pp. 1621 ◽  
Author(s):  
Jian Zhou ◽  
Enming Li ◽  
Haixia Wei ◽  
Chuanqi Li ◽  
Qiuqiu Qiao ◽  
...  
Keyword(s):  
Shear Strength ◽  
Random Forests ◽  
Estimation Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Mean Estimation ◽  
Rockfill Materials ◽  
Complex Relationships ◽  
Artificial Neural Network Ann ◽  
Models Validation

The shear strength of rockfill materials (RFM) is an important engineering parameter in the design and audit of geotechnical structures. In this paper, the predictive reliability and feasibility of random forests and Cubist models were analyzed by estimating the shear strength from the relative density, particle size, distribution (gradation), material hardness, gradation and fineness modulus, and confining (normal) stress. For this purpose, case studies of 165 rockfill samples have been applied to generate training and testing datasets to construct and validate the models. Thirteen key material properties for rockfill characterization were selected to develop the proposed models. Validation and comparison of the models have been performed using the root mean square error (RMSE), coefficient of determination (R2), and mean estimation error (MAE) between the measured and estimated values. A sensitivity analysis was also conducted to ascertain the importance of various inputs in the prediction of the output. The results demonstrated that the Cubist model has the highest prediction performance with (RMSE = 0.0959, R2 = 0.9697 and MAE = 0.0671), followed by the random forests model with (RMSE = 0.1133, R2 = 0.9548 and MAE= 0.0665), the artificial neural network (ANN) model with (RMSE = 0.1320, R2 = 0.9386 and MAE = 0.0841), and the conventional multiple linear regression technique with (RMSE = 0.1361, R2 = 0.9345 and MAE = 0.0888). The results indicated that the Cubist and random forests models are able to generate better predictive results of the shear strength of RFM than ANN and conventional regression models. The Cubist model was considered to be more promising for interpreting the complex relationships between the influential properties of RFM and the shear strengths of RFM to some extent, which can be extremely helpful in estimating the shear strength of rockfill materials.

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