Optimization of ANFIS with GA and PSO estimating α ratio in driven piles

Moayedi, Hossein; Raftari, Mehdi; Sharifi, Abolhasan; Jusoh, Wan Amizah Wan; Rashid, Ahmad Safuan A.

doi:10.1007/s00366-018-00694-w

Cited by 118 publications

(59 citation statements)

References 33 publications

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“…Similar to former research, the database was divided, randomly, into two sections-training and testing the approaches, to a ratio between 80 and 20 percentages, respectively. These ratios are properly described in similar works (e.g., Dieu Tien et al [38] and Moayedi et al [32]). To assess the most suitable approximation networks, we compared the improved models with each other.…”

Section: Resultssupporting

confidence: 53%

“…They showed that the four major parameters in the algorithm of PSO were: (a) Population number, (b) private learning factor (which is denoted with C 1 and the key learning coefficient determined by C 2 ), and (c) inertial weight (I w ). Note that the PSO-ANN method is well described in other investigations like Alsarraf et al [31] and Moayedi et al [32]. The PSO-ANN method begins with installing the initialization particle set and keeps selecting the particles with new adjustment of a location to each particle (for instance, providing an initial bias and also weight).…”

Section: Data Collectionmentioning

confidence: 99%

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The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes

Moayedi

Bui

Gör

et al. 2019

IJGI

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In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a conventional ANN and adaptive neuro-fuzzy inference system (ANFIS) training solutions. The database used with 504 training datasets (e.g., a range of 80%) and testing dataset consists of 126 items (e.g., 20% of the whole dataset). Moreover, variables of the ANN method (for example, nodes number for each hidden layer) and the algorithm of PSO-like swarm size and inertia weight are improved by utilizing a total of 28 (i.e., for the PSO-ANN) trial and error approaches. The key properties were fed as input, which were utilized via the analysis of OptumG2 finite element modelling (FEM), containing undrained cohesion stability of the baseline soil (Cu), angle of the original slope (β), and setback distance ratio (b/B) where the target is selected factor of safety. The estimated data for datasets of ANN, ANFIS, and PSO-ANN models were examined based on three determined statistical indexes. Namely, root mean square error (RMSE) and the coefficient of determination (R2). After accomplishing the analysis of sensitivity, considering 72 trials and errors of the neurons number, the optimized architecture of 4 × 6 × 1 was determined to the structure of the ANN model. As an outcome, the employed methods presented excellent efficiency, but based on the ranking method, the PSO-ANN approach might have slightly better efficiency in comparison to the algorithms of ANN and ANFIS. According to statistics, for the proper structure of PSO-ANN, the indexes of R2 and RMSE values of 0.9996, and 0.0123, as well as 0.9994 and 0.0157, were calculated for the training and testing networks. Nevertheless, having the ANN model with six neurons for each hidden layer was formulized for further practical use. This study demonstrates the efficiency of the proposed neuro model of PSO-ANN in estimating the factor of safety compared to other conventional techniques.

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Section: Resultssupporting

confidence: 53%

Section: Data Collectionmentioning

confidence: 99%

The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes

Moayedi

Bui

Gör

et al. 2019

IJGI

Self Cite

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“…In the case of this study, different population sizes for GA, and a different number of decades for ICA were tested within 1000 iterations. Moreover, similar to Koopialipoor et al [39], the most appropriate values for other parameters have been determined by a trial and error process. Table 2 denotes the results of the mentioned process.…”

Section: Finding the Optimal Structure Of The Intelligent Modelsmentioning

confidence: 99%

“…Sci. 2019, 9, x FOR PEER REVIEW 13 of 26 Koopialipoor et al [39], the most appropriate values for other parameters have been determined by a trial and error process. Table 2 denotes the results of the mentioned process.…”

Section: Finding the Optimal Structure Of The Intelligent Modelsmentioning

confidence: 99%

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

et al. 2019

Self Cite

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Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.

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“…In this regard, artificial intelligence (AI) applications are considered useful, not only as robust techniques in the mining field but also in many other areas (e.g., civil engineering, fuel, and energy, and environment) [2,4,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. An overview of the literature related to PPV prediction showed that many AI models have been developed and proposed, as listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

Nguyen

Choi

Bui

et al. 2019

Sensors

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In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation.

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Optimization of ANFIS with GA and PSO estimating α ratio in driven piles

Cited by 118 publications

References 33 publications

The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes

The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

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