Genetic Algorithm-based Artificial Neural Network Research Articles

A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network

A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network

Numerical and experimental investigation of the dynamic mechanical behavior of precipitation-strengthed NiCoCrSi0.3C0.048 medium-entropy alloy

An extended crystal plasticity model and an optimized neural network are established to investigate the mechanical behavior of CoCrNiSi0.3C0.048 Medium entropy alloy (MEA) with a three-level hierarchical precipitation structure under dynamic compressive deformation at a variety of ranges of strain rates. The interaction between the matrix and first-level precipitates is described by a presented hybrid representative volume element RVE model, involving the effects of volume fraction (Vf), radius and geometric distribution on heterogeneous deformation. Effects of second- and third-level precipitates are realized through the extended crystal plasticity constitutive model, reflecting the motion mechanism of dislocations near the precipitates in the form of initial slip resistance. Meanwhile, this study investigates the influence of the presence of primary precipitates on the initiation of slip systems within special grains with Goss or S orientations in the matrix. Additionally, it provides a more in-depth explanation of the underlying principles behind the suppression of Goss texture formation by the precipitates from the perspective of slip systems analysis. The changing of volume fractions affects flow stress evolution. Avoiding suffering from expensive computational consumption by the crystal plasticity finite element method, an artificial neural network model optimized through a genetic algorithm, is presented to predict stress-strain responses and texture evolution results under varying compression strain rates. A reliable dataset derived from crystal plasticity finite element models and experimental results is utilized to train, test, and validate the genetic algorithm-based artificial neural network model. This model demonstrates good prediction capability in dynamic mechanical behavior and texture evolution, showcasing the feasibility and efficacy of the proposed neural network model. Compared to the CPFEM method, neural network models can greatly improve computational efficiency.

Rainfall Prediction using Hybridized Genetic Algorithm-Based Artificial Neural Network (GAANN) and Genetic Algorithm-Based Support Vector Machine (GA-SVM) Models.

Rainfall Prediction using Hybridized Genetic Algorithm-Based Artificial Neural Network (GAANN) and Genetic Algorithm-Based Support Vector Machine (GA-SVM) Models.

Machine learning-assisted constitutive modeling of a novel powder metallurgy superalloy

The flow behavior and microstructure evolution during thermomechanical processing (TMP) of powder metallurgy superalloys are very important for their service behavior. In this contribution, a machine learning-assisted physical model was proposed to predict the flow stress, dynamic recrystallization (DRX) kinetic, and grain size evolution of a novel FGH4113A superalloy during the TMP. The work hardening and dynamic softening stages were modeled separately to predict the flow stress. The novel aspect of the proposed model is that the strong coupled effects of deformation temperature, strain rate, true stain, and primary γ' precipitates on the DRX kinetic were quantitatively described by the genetic algorithm-based artificial neural network (GA-ANN). Besides, the grain size modeling considered the pinning effect by primary γ' precipitates and the deformation-induced DRXed grain growth. Comparing the experimental and predicted flow curves, DRX fraction, and average grain size reveals that the proposed model has a preferable prediction accuracy than the conventional model. Then the specific DRX kinetic and grain size evolution characteristics were revealed. The promoting effect of strain rate on the DRX degree was gradually increased with the deformation temperature. Finally, the 3D processing map based on the developed model was established to guide TMP's optimized design and microstructure control. Three domains were divided and discussed: grain refinement, constant, and growth. The optimal processing window is defined as 1000–1050 °C/0.001–0.006 s−1 and 1075–1100 °C/0.008–0.01 s−1.

Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models

Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation.

Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems

Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.

Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

Precise design and characteristics prediction of Ultra-High Performance Concrete (UHPC) based on artificial intelligence techniques

Recently, with the rapid development of artificial intelligence (AI) techniques, there is a strong motivation to promote the intelligent development of the Ultra-High Performance Concrete (UHPC). To achieve this goal, this paper addresses an approach for precise design and characteristics prediction of UHPC by employing the Modified Andreasen and Andersen (MAA) model and Genetic Algorithm based Artificial Neural Network (GA-ANN) technique. Herein, 80 mixtures in total are conducted as a training dataset, and then a GA-ANN model is created for characteristics prediction of UHPC, which exhibits significant superiorities in fitting goodness and prediction accuracy compared to other classical prediction models. Furthermore, a prediction software based on GA-ANN technology with an efficient Graphical User Interface (GUI) is developed. Finally, a novel method for mix-design of UHPC by the use of MAA and GA-ANN models is proposed as follows: 1) conduct preliminary mixture design by MAA model; 2) further optimize the mixture by GA-ANN according to the property requirements. In general, a new UHPC with dense particle packing skeleton can be precisely designed by this method, which effectively verifies the feasibility of the application of AI technique in the UHPC field.

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms

Developing trustworthy rainfall-runoff (R-R) models can offer serviceable information for planning and managing water resources. Use of artificial neural network (ANN) in adopting such models and predicting changes in runoff has become popular among many hydrologists from a long time. However, since the optimization is the most significant phase in ANN training, researchers’ attentiveness has been attracted to the ANN’s biggest problem, i.e. its susceptibility of being blocked in local minima. Consequently, use of genetic algorithms (GA), particle swarm optimization (PSO), firefly algorithm (FFA) and improved particle swarm optimization (IPSO) approaches to increase the performance of ANN, have gained remarkable interest among distinct modern heuristic optimization approaches. In this paper, the capability of four improved ANN methods, hybrid GA-based ANN, PSO-based ANN, FFA-based ANN and IPSO-based ANN in modeling rainfall-runoff (R-R) is investigated. IPSO has been used in order to increase the ability of PSO, where the new positions of particles are dynamically adjusted using two procedures which is given form the velocity obtained by PSO and proposed velocity in IPSO. The random normal grated number with a dynamical scale factor is used to compute the new position of the best particles in proposed velocity. Daily R-R data from six stations distributed in the Seybouse watershed located in semi-arid region in Algeria were used in models’ development. The selection of the input data sets was carried out using the autocorrelation, partial autocorrelation and cross correlation functions. The results of the four hybrid models were compared via performance metrics, viz., Root Mean Square Error (RMSE), Pearson’s correlation coefficient (R), Nash Sutcliffe Efficiency coefficient (NSE), and via graphical analysis (scatter plots, time series and Taylor diagram). Outcomes of the analysis at all study stations disclosed that all the ANN models enhanced with IPSO overachieved the GA-based ANN, PSO-based ANN and FFA-based ANN models in estimating runoff for both training and testing periods. The outcomes of the study indicate that the IPSO hybrid metaheuristic algorithm is the best technique in improving ANN capability in modeling daily R-R.

Modeling of vacuum‐ and ultrasound‐assisted osmodehydration of carrot cubes followed by combined infrared and spouted bed drying using artificial neural network and regression models

AbstractThis research seeks to understand the effects of using various vacuum pretreatments (0 and 200 mBar) and different ultrasound power levels (0 and 130 W) on osmodehydration and spouted bed drying of carrot cubes. Effects of using diverse air‐drying temperatures (55, 65 and 75°C), infrared power (0 and 250 W) and the presence of Teflon beads as inert particles (0 and 30% w/w), were also investigated. The effects of above‐mentioned parameters on drying time and physicochemical characteristics of dried carrot samples including hardness, shrinkage, total carotenoid content, rehydration ratio and total color difference (∆E) were examined. The results explicitly demonstrated that the utilization of osmodehydration in conjunction with ultrasound and vacuum (UVOD) had shorter drying time compared to those just treated with osmotic dehydration. The highest total carotenoid content and the lowest ∆E were belonged to the samples exposed to UVOD. The efficiency of artificial neural network (ANN), genetic algorithm based ANN (GANN) and Regression mathematical models were investigated to determine the nonlinear relationship between the inputs and outputs variables. To find the efficiency of all models, mean square error were compared. The results indicate that the ANN model could provide more reliable prediction over the other models.Practical applicationCombined IR‐convective drying has gained increased attention in the recent years because of the fast and efficient transfer of heat, cheap processing cost, and uniform heat transfer, which resulted in products with better organoleptic properties than common drying processes. The combination of hot air and IR radiation resulted in a synergistic effect, leading to more efficient drying than either individual convective or IR drying. Vegetables with high levels of moisture content can be effectively dried using pretreatments such as ultrasonically vacuum‐assisted osmotic dehydration, which reduced the drying time.

A novel approach for developing a green Ultra-High Performance Concrete (UHPC) with advanced particles packing meso-structure

This paper presents a new approach for designing a green Ultra-High Performance Concrete (UHPC) with advanced particles packing meso-structure. To achieve this goal, two mathematical models based on D-Optimal Mixture Design (DOMD) and Genetic Algorithm based Artificial Neural Network (GA-ANN) are firstly established. After detailed comparison, it is demonstrated that the GA-ANN has higher accuracy and practicability compared to DOMD. To experimentally verify that the GA-ANN approach is suitable to be utilized in producing green UHPC, some UHPC specimens are developed based on this model, and their properties (workability, mechanical properties, pore structures and ecological characteristics) are investigated. The obtained experimental results show that the optimized UHPC has higher wet packing density and lower porosity, leading to excellent mechanical properties. Moreover, based on economic and environmental evaluation, the newly developed UHPC exhibits lower cost and environmental impact, something that makes it a sustainable construction product.

Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity

A commonly-encountered problem in foundation design is the reliable prediction of the pile bearing capacity (PBC). This study is planned to propose a feasible soft computing technique in this field i.e.; the Gaussian process regression (GPR) for the PBC estimation. The established database includes 296 number of dynamic pile load test in the field where the most influential factors on the PBC were selected as input variables. Several GPR models were designed and built. These models were assessed using three performance indices namely value account for (VAF), coefficient of determination (R2) and system error. To have a comparison, a genetic algorithm-based artificial neural network (GA-based ANN) model was also employed. It was found that the GPR-based model with VAF value of 86.41%, R2 of 0.84 and system error of 0.2006 is capable enough to predict the PBC and outperforms the GA-based ANN model. The results showed that the GPR can be utilized as a practical tool for the PBC estimation.

A parametric study of 3D printed polymer gears

The selection of printing parameters for 3D printing can dramatically affect the dynamic performance of components such as polymer spur gears. In this paper, the performance of 3D printed gears has been optimised using a machine learning process. A genetic algorithm (GA)–based artificial neural network (ANN) multi-parameter regression model was created. There were four print parameters considered in 3D printing process, i.e. printing temperature, printing speed, printing bed temperature and infill percentage. The parameter setting was generated by the Sobol sequence. Moreover, sensitivity analysis was carried out in this paper, and leave-one cross validation was applied to the genetic algorithm-based ANN which showed a relatively accurate performance in predictions and performance optimisation of 3D printed gears. Wear performance of 3D printed gears increased by 3 times after optimised parameter setting was applied during their manufacture.

Genetic algorithm based ANN to predict compressive strength of siphon for different fiber volume fraction

This paper presents the applicability of Genetic Algorithm based Artificial Neural Network (GAANN) for predicting Compressive strength of Slurry Infiltrated Fibrous Concrete (SIFCON) prepared with manufactured sand for different fibre volume fraction (8%, 10% and 12%) as input vector. The network has been trained with data obtained from experimental work. The proposed GAANNs model is successfully used for predicting compressive strength of SIFCON (output vector) for various fibre volume fractions (2%, 4%, 6%, 14%, 16%, 18%, 20% and 22%) at 7 days, 28 days and 56 days of curing respectively. After successful learning GA based ANN model pre- dicted the compressive strength property satisfying all the constrains with an accuracy of about 85%.The various stages involved in the development of genetic algorithm based neural network are addressed in depth in this paper.

Evolving neural network using genetic algorithm for mining method evaluation in thin coal seam working face

Mining method selection is one of the non-linear decisions made by mining engineers. The artificial neural network (ANN) is a commonly used method for this decision-making. This paper investigated the effectiveness of ANN for mining method evaluation. The back-propagation (BP) algorithm was selected. The input variables are the geological conditions and face parameters. The output ones are the mining method and the production. Synthesising iterative efficiency and MSE, BP with VSS algorithm by appending MT was the priority to the evaluation. For better results, ANN optimised through genetic algorithm (GA) was also applied and tested. As a result, the mean square errors (MSE) for ANN and GA-based ANN at testing stage are 0.54 and 0.08, respectively. Moreover, the correlation coefficient R2 values are 0.99 and 0.96. The gained results indicated that GA-based ANN was more promising for mining method evaluation.

A genetic algorithm-based artificial neural network model with TOPSIS approach to optimize the engine performance

ABSTRACTThis paper describes the application of an artificial neural network, genetic algorithm and analytic network process-technique for order preference by similarity to ideal solution for the selection of the optimum fuel blend. A single-cylinder, constant-speed direct-injection diesel engine with a rated output of 4.4 kW was used for exploratory analysis at different load conditions. Ten objectives – brake thermal efficiency, maximum rate of pressure rise, NOx, CO2, CO, HC, smoke, exhaust gas temperature, ignition delay and combustion delay were considered. The proposed ANN model is integrated with GA and the hybrid multi-criteria decision-making technique of ANP-TOPSIS to evaluate the optimum blend. First the ANN model was developed to predict the performance, combustion and emission parameters of the engine. A multi-layer perception network was used for non-linear mapping between input and output parameters. The performance of the ANN model is determined and shows the efficiency of the model to predict the performance, emission and combustion parameters, with a determination coefficient of 0.9627. Second, GA was used to determine the optimum load and blend based on the predicted ANN output parameter. Third, an approach based on the TOPSIS method was used for finding the best blend from the observed optimum parameters of GA.

Porosity- and reliability-based evaluation of concrete-face rock dam compaction quality

Abstract Compaction quality evaluation of concrete-faced rockfill dams (CFRDs) is a complex nonlinear process. Conventional evaluation methods depend on random spot tests, and porosity is considered as the main evaluation index. However, reliability of results is neglected in existing studies. Considering compaction parameters, material properties and their variability characteristics, an evaluation approach of CFRD compaction quality based on porosity and reliability is proposed. Reliability analysis is introduced to measure the variability in highly variable factors and calculate the porosity- and reliability-based index. Porosities of the entire work area are predicted using an elitist genetic algorithm-based artificial neural network, which are consistent with the measured values with an error

Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

In this study, artificial neural networks (ANNs) have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D), the ratio of the cold outlet diameter to the tube diameter(d/D), inlet pressure(P), and cold mass fraction (Y) on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA) has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y). The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network (GAANN) technique for Australia

This paper focuses on the development and testing of the genetic algorithm (GA)-based regional flood frequency analysis (RFFA) models for eastern parts of Australia. The GA-based techniques do not impose a fixed model structure on the data and can better deal with nonlinearity of the input and output relationship. These nonlinear techniques have been applied successfully in many hydrologic problems; however, there have been only limited applications of these techniques in RFFA problems, particularly in Australia. A data set comprising of 452 stations is used to test the GA for artificial neural networks (ANN) optimization known as GAANN. The results from GAANN were compared with the results from back-propagation for ANN optimization known as BPANN. An independent testing shows that both the GAANN and BPANN methods are quite successful in RFFA and can be used as alternative methods to check the validity of the traditional linear models such as quantile regression technique.