Differential Evolution Neural Network Research Articles

A predictive equation for residual strength using a hybrid of subset selection of maximum dissimilarity method with Pareto optimal multi-gene genetic programming

More accurate and reliable estimation of residual strength friction angle (ϕr) of clay is crucial in many geotechnical engineering applications, including riverbank stability analysis, design, and assessment of earthen dam slope stabilities. However, a general predictive equation for ϕr, with applicability in a wide range of effective parameters, remains an important research gap. The goal of this study is to develop a more accurate equation for ϕr using the Pareto Optimal Multi-gene Genetic Programming (POMGGP) approach by evaluating a comprehensive dataset of 290 experiments compiled from published literature databases worldwide. A new framework for integrated equation derivation proposed that hybridizes the Subset Selection of Maximum Dissimilarity Method (SSMD) with Multi-gene Genetic Programming (MGP) and Pareto-optimality (PO) to find an accurate equation for ϕr with wide range applicability. The final predictive equation resulted from POMGGP modeling was assessed in comparison with some previously published machine learning-based equations using statistical error analysis criteria, Taylor diagram, revised discrepancy ratio (RDR), and scatter plots. Base on the results, the POMGGP has the lowest uncertainty with U95 = 2.25, when compared with Artificial Neural Network (ANN) (U95 = 2.3), Bayesian Regularization Neural Network (BRNN) (U95 = 2.94), Levenberg-Marquardt Neural Network (LMNN) (U95 = 3.3), and Differential Evolution Neural Network (DENN) (U95 = 2.37). The more reliable results in estimation of ϕr derived by POMGGP with reliability 59.3%, and resiliency 60% in comparison with ANN (reliability = 30.23%, resiliency = 28.33%), BRNN (reliability = 10.47%, resiliency = 10.39%), LMNN (reliability = 19.77%, resiliency = 20.29%) and DENN (reliability = 27.91%, resiliency = 24.19%). Besides the simplicity and ease of application of the new POMGGP equation to a broad range of conditions, using the uncertainty, reliability, and resilience analysis confirmed that the derived equation for ϕr significantly outperformed other existing machine learning methods, including the ANN, BRNN, LMNN, and DENN equations.

Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural network. Due to the contradiction between population diversity and convergence rate in the optimization of complex neural networks by using differential evolution, a gravitational search algorithm and differential evolution is proposed to accelerate the convergence rate of differential evolution population driven by gravity. Gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population. Compared with the standard differential evolution, the convergence speed and accuracy of gravitational search algorithm and differential evolution are significantly improved, which has better optimization stability. The calculation results show that the output accuracy of this gravitational and differential evolution neural network can meet the calculation requirements of the positioning control of demolition robot’s manipulator. The optimization using gravitational search algorithm and differential evolution is done with the connection weights of a neural network in this article, and as similar techniques can be applied to the other hyperparameter optimization problem. Moreover, such an inverse calculation method can provide a reference for the autonomous positioning of large hydraulic series manipulator, so as to improve the robotization level of construction machinery.

Prediction of lateral load capacity of piles using extreme learning machine

This study presents the development of a predictive model for the lateral load capacity of pile in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural networks (ANN) (Bayesian regularization neural network (BRNN), differential evolution neural network (DENN)) are also developed to compare the ELM model with them and available empirical models in terms of different statistical criteria. A ranking system is presented to evaluate the present models for identifying the “best” model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds

A simple self-adaptive version of the differential evolution algorithm was applied for simultaneous architectural and parametric optimization of feed-forward neural networks, used to classify the crystalline liquid property of a series of organic compounds. The developed optimization methodology was called self-adaptive differential evolution neural network (SADE-NN) and has the following characteristics: the base vector used is chosen as the best individual in the current population, two differential terms participate in the mutation process, the crossover type is binomial, a simple self-adaptive mechanism is employed to determine the near-optimal control parameters of the algorithm, and the integration of the neural network into the differential evolution algorithm is performed using a direct encoding scheme. It was found that a network with one hidden layer is able to make accurate predictions, indicating that the proposed methodology is efficient and, owing to its flexibility, it can be applied to a large range of problems.

Differential evolution for neural networks learning enhancement.

In this paper ,we use new treatment ,Differential Evolution,, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. In ANN, there are many elements need to be considered, and these include the number of input nodes, hidden nodes, output nodes, learning rate, momentum rate, bias parameter, minimum error and activation/transfer functions. Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. The results have revealed that DENN has given quite promising results in terms of convergence rate and smaller errors compared to PSONN and GANN.

Classification of slopes and prediction of factor of safety using differential evolution neural networks

Slope stability analysis is one of the most important problems in geotechnical engineering. The development in slope stability analysis has followed the development in computational geotechnical engineering. This paper discusses the application of different recently developed artificial neural network models to slope stability analysis based on the actual slope failure database available in the literature. Different ANN models are developed to classify the slope as stable or unstable (failed) and to predict the factor of safety. The developed ANN model is found to be efficient compared with other methods like support vector machine and genetic programming available in literature. Prediction models are presented based on the developed ANN model parameters. Different sensitivity analyses are made to identify the important input parameters.

Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology

The main objective of this research is to automatically design Artificial Neural Network models with sigmoid basis units for multiclassification tasks in predictive microbiology. The classifiers obtained achieve a double objective: a high classification level in the dataset and high classification levels for each class. The Memetic Pareto Differential Evolution Neural Network chosen to learn the structure and weights of the Neural Networks is a Differential Evolutionary approach based on the Pareto Differential Evolution multiobjective evolutionary algorithm. The Pareto Differential Evolution algorithm is augmented with a local search using the improved Resilient Backpropagation with backtracking–iRprop + algorithm. To analyze the robustness of this methodology, it has been applied to two complex classification problems in predictive microbiology (Staphylococcus aureus and Shigella flexneri). The results obtained show that the generalization ability and the classification rate in each class can be more efficiently improved within this multiobjective algorithm.

A differential evolution based neural network approach to nonlinear system identification

This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input–multi-output system (TRMS) to verify the identification performance.