Differential Evolution for Neural Networks Optimization

Baioletti, Marco; Bari, Gabriele Di; Milani, Alfredo; Poggioni, Valentina

doi:10.3390/math8010069

Cited by 43 publications

(16 citation statements)

References 36 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the trained model, after inputting the input data, the predicted value of the sample can be calculated by the neural network. With the increasing size and complexity of data, neuroevolution is used to optimize the strengths of neural connections and the structure of the network, such as conventional neuro evolution (CNE) [17] and differential evolution for neural networks (DENN) [18]. Moreover, some scholars have combined neural networks with data feature extraction technology to improve the fault recognition ability [19,20].…”

Section: Methodsmentioning

confidence: 99%

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Liu

2020

Symmetry

View full text Add to dashboard Cite

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

show abstract

Section: Methodsmentioning

confidence: 99%

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Liu

2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…As revealed in the literature, a key aspect of using evolutionary algorithms for optimizing AI models is to study the relationship between population size and problem dimensionality [117][118][119][120]. In many other evolutionary algorithms such as differential evolution, the number in the population is recommended to be 7-10 times the number of inputs [121,122].…”

Section: Optimization Of Weight Parameters Of Fnn Using the Iwo Technmentioning

confidence: 99%

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

Nguyen

Tran

et al. 2020

Materials

View full text Add to dashboard Cite

Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (P u ) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict P u was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN-IWO model and improve its prediction performance. The results showed that the FNN-IWO algorithm is an excellent predictor of P u , with a value of R 2 of up to 0.979. The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R 2 , respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P u in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.Materials 2020, 13, 1205 2 of 25 larger energy absorption capacity [7], convenient construction [11], economy of materials [12][13][14], and excellent seismic and refractory performance [15]. In particular, this type of structure can reduce the environmental burden by removing formwork [16], reusing steel pipes, and using high quality concrete with recycled aggregate [17]. The characteristics of CFST are that the steel material is located far from the central axis so the rigidity of the column is very large, and thus it also contributes to increasing the moment of inertia of the structure [5,18]. The ideal form of concrete core works against the compressive load and hinders the local buckling state of the steel pipe. Therefore, the CFST structures are often used in locations subject to large compressive loads [9,15,19]. The CFST columns are mainly divided into square columns, round columns, and rectangular columns, based on different cross-sectional forms [15]. In particular, the square and rectangular CFST columns have the advantage of easy connection and reliable work with other structural members such as beams, walls, and panels [20]. Compared with square CFST columns, rectangular columns have irregular bending stiffness along different axes, so this type of column is suitable for the mechanical behavior of members including arch ribs, pillars, abutments, and piers, and other structural members under load actions vary greatly from vertical to horizontal [6]. Because the scope of application of rectangular CFST columns is quite wide and this column is mainly subjected to compression, the main purpose of the paper is to analyze and evaluate the ultimate bearing capacity of...

show abstract

“…Differential evolution (DE), first proposed by Storn and Price (Storn and Price, 1997), is a simple yet powerful evolutionary algorithm. DE has exhibited notable performance due to its simple structure, rapid convergence speed as well as strong robustness and has been applied successfully in many domains of science and engineering such as neural network (Su et al, 2019;Baioletti et al, 2020), power system (Sakr et al, 2017;Reddy and Bijwe, 2019), medical aspect (Nunes et al, 2017;Song et al, 2019;Hosny et al, 2020), image processing (Paul and Das, 2015;Tarkhaneh and Shen, 2019) and many other practical optimization problems (Balamurugan and Muthukumar, 2019;Huang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Dual Mutations Collaboration Mechanism with Elites Guiding and Inferiors Eliminating Techniques for Differential Evolution

Sun

et al. 2021

Preprint

View full text Add to dashboard Cite

Differential Evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.

show abstract

Differential Evolution for Neural Networks Optimization

Cited by 43 publications

References 36 publications

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

Dual Mutations Collaboration Mechanism with Elites Guiding and Inferiors Eliminating Techniques for Differential Evolution

Contact Info

Product

Resources

About