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Novel evolutionary-optimized neural network for predicting landslide susceptibility

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年第7期26卷 17687-17719页

作者： Adnan Ikram, Rana Muhammad Khan, Imran Moayedi, Hossein Ahmadi Dehrashid, Atefeh Elkhrachy, Ismail Nguyen Le, Binh Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Univ Haripur Dept Econ Haripur Pakistan Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam Univ Kurdistan Fac Nat Resources Dept Climatol Sanandaj Iran Univ Kurdistan Kurdistan Studies Inst Dept Zrebar Lake Environm Res Sanandaj Iran Najran Univ Coll Engn Civil Engn Dept Najran 66454 Saudi Arabia

In order to mitigate/prevent the risks of landslides, one of the essential tools that can be used to manage and plan the development of human settlements is landslide susceptibility. The two metaheuristic algorithms explored in this paper are the SCE and VSA algorithms used to optimize the artificial neural network (ANN) model. By integrating the two algorithms with the artificial neural network model, we try to determine its optimum computational parameters in order to generate the landslide susceptibility mapping for the Kurdistan province in Iran. Sixteen causative factors of landslides are included in the spatial database. The landslide susceptibility maps were generated in a GIS medium, and in order to evaluate the employed predictive models, the criterion of the area under the curve (AUC) was employed. This investigation includes 1072 landslide events, which are divided as follows: one-third as testing data and two-thirds as training data (i.e., a 75:25 ratio). The results indicate that after using the abovementioned algorithms, the AUC increased noticeably from 0.708 to 0.788 for SCE-MLP and from 0.744 to 0.818 for VSA-MLP in the training phase. The criterion of the area under the curve was utilized in order to evaluate the accuracy of the employed probabilistic models. Incidentally, the comparable AUCs calculated for the VSA-MLP testing databases and the obtained AUCs were 0.818, 0.801, 0.791, 0.786, 0.784, 0.778, 0.777, 0.776, 0.754 and 0.744, respectively for population size in training databases equal to 50, 350, 500, 150, 450, 200, 400, 300, 100 to and 250. Also, in case of SCE-MLP, the training and testing AUC were found (0.878, 0.865, 0.851, 0.850, 0.819, 0.816, 0.815, 0.781, 0.760, and 0.756) and (0.788, 0.782, 0.759, 0.744, 0.727, 0.720, 0.718, 0.713 and 0.708). The best fit for swarm size conditions of the SCE-MLP and VSA-MLP model showed 150 and 350, respectively. The acquired results indicate that the VSA-ANN model has a better predictive capabili

关键词： Neural metaheuristic algorithms Landslide susceptibility optimization algorithms Shuffled complex evolution (SCE) Vortex search algorithm (VSA)

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Driven piles' load capacity estimation by applying comparative regression methods

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 31-42页

作者： Liu, Tianyu Weihai Ocean Vocat Coll Dept Marine Engn Weihai 264200 Shandong Peoples R China

For deep foundation load capacity estimation (Q(t)) in geotechnical engineering, several empirical and theoretical frameworks have been proposed. In this scenario, simulations are based on physical constraints and algebraic frames. Regression analyses are excellent computing systems which effectively approximate and represent numerous actions. Consequently, it was aimed to develop the novel and hybrid regression models using support vector regression (SVR) for estimating driven piles' load capacity (Q(t)). For designing the most reliable SVR analysis, three optimization procedures named arithmetic optimization algorithm (AOA), multiverse optimizer (MVO), and particle swarm optimization (PSO) were combined with SVR. For the SVR models, the aim was to specify the optimal value of control parameters. In terms of statistical effectiveness indicators such as R-2, RMSE, RRSE, and VAF, it is clear that all hybrid and optimized SVR analysis with optimization metaheuristic algorithms conduct admirably in the training and testing categories, with minimum R-2 of 0.9062 and 0.8855, respectively, demonstrating a strong association between actual and simulated Q(t). Between the created regression analysis, the AOA-SVR framework works most properly compared to MVO-SVR followed by PSO-SVR network by increasing the value of R-2 and VAF, and reducing the value of RMSE and RRSE. Finally, the descriptions and justifications exhibit that AOA metaheuristic algorithm is extremely reliable and robust in the determination procedure of SVR's determinative parameters.

关键词： Driven piles Load-bearing capacity Simulation Support vector regression optimization algorithms

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optimization algorithms for massive accesses of MTC devices in LTE-A networks

Optimization algorithms for massive accesses of MTC devices ...

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10th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2014)

作者： Chen Chen Guangliang Ren Qiwei Wang State Key Laboratory of Integrated Service Networks ISN Xidian University Xi'an China

ISBN: (纸本)9781510804166

The massive random accesses of machine type communication (MTC) in current wireless networks (e.g. LTE-A, WiMAX) are a challenging and urgent issue. In this paper, the random access performances in LTE-A networks are first analyzed in terms of success probability, access delay, physical random access channel efficiency, and the number of retransmissions. Based on the analysis, two QoS-based optimization algorithms are then proposed for the random access of massive MTC devices. Simulation results show that the proposed algorithms achieve better performances than non-optimized random access, providing required QoS guarantees for each MTC application, and meanwhile optimizing the spectrum efficiency and energy efficiency.

关键词： Internet of Things LTE-A Machine Type Communication Random Access RANDOM ACCESS optimization algorithms Internet of things Minesite Technical Committees spectral efficiency

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A smart multiplexed microRNA biosensor based on FRET for the prediction of mechanical damage and storage period of strawberry fruits

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PLANT MOLECULAR BIOLOGY 2025年第2期115卷 1-10页

作者： Vakilian, Keyvan Asefpour Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Engn Gorgan Iran

Today, measuring the concentration of various microRNAs in fruits has been introduced to model the storage conditions of agricultural products. However, there is a limiting factor in the extensive utilization of such techniques: the existing methods for measuring microRNA sequences, including PCR and microarrays, are time-consuming and expensive and do not allow for simultaneous measurement of several microRNAs. In this study, a biosensor based on the F & ouml;rster resonance energy transfer (FRET) of fluorescence dyes that can lead to the hybridization of oligonucleotide probes labeled with such dyes by using an excitation wavelength has been used to simultaneously measure microRNAs. Three microRNA compounds, i.e., miRNA-164, miRNA-167, and miRNA-399a, which play significant roles in the postharvest characteristics of strawberry fruits were measured. The simultaneous measurement was performed using three fluorescence dyes which exert various emission wavelengths at 570, 596, and 670 nm. In the following, machine learning methods including artificial neural networks (ANNs) and support vector machines (SVMs), with hyperparameter values optimized with the help of metaheuristic optimization algorithms, were used to predict the amount of mechanical loading on strawberry fruits and their storage period having the microRNA concentrations. The results showed that the SVM with Gaussian kernel, which was optimized by the Harris hawks optimization, is capable of predicting the mechanical stress and storage period of strawberry fruits with a coefficient of determination (R2) of 0.89 and 0.92, respectively. The findings of this study reveal the application of combining FRET-based biosensors and machine learning methods in fruit storage quality assessment.

关键词： Support vector machine optimization algorithms microRNA biosensor FRET

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Fast Generation of Feasible Trajectories in Direct Optimal Control

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1168-1173页

作者： Kiessling, David Baumgartner, Katrin Frey, Jonathan Decre, Wilm Swevers, Jan Diehl, Moritz Katholieke Univ Leuven Dept Mech Engn B-3001 Leuven Belgium Flanders Make KU Leuven B-3001 Leuven Belgium Univ Freiburg Dept Microsyst Engn D-79110 Freiburg Germany Univ Freiburg Dept Math D-79110 Freiburg Germany

This letter examines the question of finding feasible points to discrete-time optimal control problems. The optimization problem of finding a feasible trajectory is transcribed to an unconstrained optimal control problem. An efficient algorithm, called FP-DDP, is proposed that solves the resulting problem using Differential Dynamic Programming preserving feasibility with respect to the system dynamics in every iteration. Notably, FP-DDP admits global and rapid local convergence properties induced by a combination of a Levenberg-Marquardt method and an Armijo-type line search. An efficient implementation of FP-DDP within acados is compared to established methods such as Direct Multiple Shooting, Direct Single Shooting, and state-of-the-art solvers.

关键词： optimization Optimal control Linear programming Heuristic algorithms Convergence Vectors Trajectory optimization algorithms numerical algorithms

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Optimising the parameters in AWJT process using a modified multi-objective DE algorithm

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International Journal of Machining and Machinability of Materials 2025年第1期27卷 19-39页

作者： Gawai, I.R. Lalwani, D.I. Department of Mechanical Engineering Sardar Vallabhbhai National Institute of Technology Ichchhanath Circle Athawa Lines Gujarat Surat395007 India

This paper presents a novel multi-objective optimisation algorithm for predicting the optimal control parameters of a radial abrasive water jet turning (AWJT) method. The objective is to maximise the material removal rate (MRR) and minimise the surface roughness (Ra) of the turned surface. The control parameters include water pressure, jet feed speed, abrasive flow rate, surface speed, and nozzle tilted angle. The proposed algorithm, called multi-objective amended differential evolution algorithm (MADEA), is a rank-based differential evolution (DE) algorithm that uses non-dominated sorting and crowding distance to select and update the solutions. The performance of MADEA is compared with six state-of-the-art multi-objective evolutionary algorithms on a set of benchmark test problems and the AWJT problem. The results show that MADEA can find better Pareto optimal solutions than the other algorithms. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： optimization algorithms

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Distributed quasi-Newton derivative-free optimization method for optimization problems with multiple local optima

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COMPUTATIONAL GEOSCIENCES 2022年第4期26卷 847-863页

作者： Gao, Guohua Wang, Yixuan Vink, Jeroen C. Wells, Terence J. Saaf, Fredrik J. F. E. Shell Global Solut US Inc 3333 HWY 6 S Houston TX 77082 USA Shell Global Solut Int BV Kessler Pk 1 NL-2288 GS Rijswijk Netherlands

The distributed Gauss-Newton (DGN) optimization method performs quite efficiently and robustly for history-matching problems with multiple best matches. However, this method is not applicable for generic optimization problems, e.g., life-cycle production optimization or well location optimization. This paper introduces a generalized form of the objective functions F(x, y(x)) = f(x) with both explicit variables x and implicit variables (or simulated responses), y(x). The split in explicit and implicit variables is such that partial derivatives of F(x, y) with respect to both x and y can be computed analytically. An ensemble of quasi-Newton optimization threads is distributed among multiple high-performance-computing (HPC) cluster nodes. The simulation results generated from one optimization thread are shared with others by updating a common set of training data points, which records simulated responses of all simulation jobs. The sensitivity matrix at the current best solution of each optimization thread is approximated by the linear-interpolation method. The gradient of the objective function is then analytically computed using its partial derivatives with respect to x and y and the estimated sensitivities of y with respect to x. The Hessian is updated using the quasi-Newton formulation. A new search point for each distributed optimization thread is generated by solving a quasi-Newton trust-region subproblem (TRS) for the next iteration. The proposed distributed quasi-Newton (DQN) method is first validated on a synthetic history matching problem and its performance is found to be comparable with the DGN optimizer. Then, the DQN method is tested on a variety of optimization problems. For all test problems, the DQN method can find multiple optima of the objective function with reasonably small numbers of iterations.

关键词： optimization algorithms Distributed quasi-Newton method Trust-region search Sensitivity matrix Information sharing mechanism

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A hybrid meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2022年第6期20卷 1581-1605页

作者： Noorian Talouki, Reza Hosseini Shirvani, Mirsaeid Motameni, Homayon Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran

Purpose Improvement of workflow scheduling in distributed engineering systems Design/methodology/approach The authors proposed a hybrid meta heuristic optimization algorithm. Findings The authors have made improvement in hybrid approach by exploiting of genetic algorithm and simulated annealing plus points. Originality/value To the best of the authors' knowledge, this paper presents a novel theorem and novel hybrid approach.

关键词： Cloud heterogeneous platforms Workflow scheduling Hybrid meta-heuristic approach Computer simulation Genetic algorithms optimization algorithms

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Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

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Soft Computing 2024年第17-18期28卷 9949-9963页

作者： Xue, Caiwen Liu, Tong Deng, Libao Gu, Wei Zhang, Baowu College of Metrology Measurement and Instrument China Jiliang University Hangzhou310018 China School of Information and Electrical Engineering Harbin Institute of Technology Shandong Weihai150006 China

Differential Evolution (DE) is a global optimization process that uses population search to find the best solution. It offers characteristics such as fast convergence time, simple and understood algorithm, few parameters, and good stability. To improve its presentation, we propose a differential evolution algorithm based on subpopulation adaptive scale and multi-adjustment strategy (ASMSDE). The algorithm separates the population into three sub-populations based on fitness scores, and different operating tactics are used depending on their characteristics. The superior population uses Gaussian disturbance, while the inferior population uses Levy flights. The intermediate population is responsible for maintaining the population's overall variety. The sizes of the three sub-populations are adaptively changed in response to evolutionary results to account for changes in individual differences over time. With the number of iterations increases and the disparities between individuals reduce, adopt a single population model instead of multi-population model in the later stage of evolution. The ASMSDE algorithm's performance is evaluated by comparing it to other sophisticated algorithms that use benchmark function optimizations. Experimental results show that the ASMSDE algorithm outperforms the comparison algorithms in the majority of circumstances, demonstrating its effectiveness and capacity to manage local optimum situations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： optimization algorithms

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Extrapolated Proportional-Integral Projected Gradient Method for Conic optimization

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IEEE CONTROL SYSTEMS LETTERS 2022年 7卷 73-78页

作者： Yu, Yue Elango, Purnanand Acikmese, Behcet Topcu, Ufuk Univ Texas Austin Oden Inst Computat Sci & Engn Austin TX 78712 USA Univ Washington Dept Aeronaut & Astronaut Seattle WA 98195 USA

Conic optimization is the minimization of a convex quadratic function subject to conic constraints. We introduce a novel first-order method for conic optimization, named extrapolated proportional-integral projected gradient method (xPIPG), that automatically detects infeasibility. The iterates of xPIPG either asymptotically satisfy a set of primal-dual optimality conditions, or generate a proof of primal or dual infeasibility. We demonstrate the application of xPIPG using benchmark problems in model predictive control. xPIPG outperforms many state-of-the-art conic optimization solvers, especially when solving large-scale problems.

关键词： optimization Gradient methods Symmetric matrices Extrapolation Mathematical models Convergence Minimization optimization algorithms infeasibility detection

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