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Hybrid Improved Self-adaptive Differential Evolution and Nelder-Mead Simplex Method for Solving Constrained Real-Parameters

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Journal of Mechanics Engineering and Automation 2013年第9期3卷 551-559页

作者： ngoc-tam bui hieu pham hiroshi hasegawa College of Systems Engineering and Science Shibaura Institute of Technology Saitama 33 7-85 70 Japan

In this paper, a new hybrid algorithm based on exploration power of a new improvement self-adaptive strategy for controlling parameters in DE （differential evolution） algorithm and exploitation capability of Nelder-Mead simplex method is presented （HISADE-NMS）. The DE has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as number of particles （NP）, scaling factor （F） and crossover control （CR）, which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depends on the characteristics of each objective function, therefore, we have to tune their value in each problem that mean it will take too long time to perform. In the new manner, we present a new version of the DE algorithm for obtaining self-adaptive control parameter settings. Some modifications are imposed on DE to improve its capability and efficiency while being hybridized with Nelder-Mead simplex method. To valid the robustness of new hybrid algorithm, we apply it to solve some examples of structural optimization constraints.

关键词： Differential evolution hybrid algorithms evolutionary computation global search local search simplex method.

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A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

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Frontiers of Structural and Civil Engineering 2022年第6期16卷 667-684页

作者： Nang Duc bui hieu Chi PHAN Tiep Duc pham Ashutosh Sutra DHAR Institute of Techniques for Special Engineering Le Quy Don Technical UniversityHanoi 100000Vietnam Faculty of Engineering and Applied Science Memorial University of NewfoundlandSt.John’sNL A1B 3X5Canada

The study proposes a framework combining machine learning(ML)models into a logical hierarchical system which evaluates the stability of the sheet wall before other *** study uses the hardening soil(HS)model to develop a 200-sample finite element analysis(FEA)database,to develop the ML ***,a system containing three trained ML models is proposed to first predict the stability status(random forest classification,RFC)followed by 1)the cantilever top horizontal displacement of sheet wall(artificial neural network regression models,RANN1)and 2)vertical settlement of soil(RANN2).The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models,based on the application of random sampling technique in the data splitting *** on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 *** implies a high performance of RFC on the database with medians of accuracy,precision,and recall,on test set are 1.0000,1.0000,and 0.92857,*** regression ANN models have coefficient of determinations on test set,as high as 0.9521 for RANN1,and 0.9988 for RANN2,*** parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.

关键词： finite element analysis cantilever sheet wall machine learning artificial neural network random forest

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Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

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International Journal of Sediment Research 2023年第1期38卷 128-140页

作者： Duong Tran Anh Ahad Hasan Tanim Daniel Prakash Kushwaha Quoc Bao pham Van hieu bui HUTECH University Center for River Harbor and Landslide Research Chittagong University of Engineering & Technology Govind Ballabh Pant University of Agriculture and Technology Institute of Applied Technology Thu Dau Mot University Faculty of Mechanical Engineering Thuyloi University

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution(GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory(LSTM) combined with the Discrete Element Method(DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in threedimensional(3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination(R~2), root mean squared error(RMSE), and mean absolute error(MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.

关键词： Numerical modeling Discrete element method LSTM Bed porosity Gravel-bed river

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