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 e...
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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
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 alg...
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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.
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 ...
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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.
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 ...
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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.
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 prob...
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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.
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 ...
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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 ...
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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.
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...
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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.
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 paramet...
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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 gr...
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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.
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