The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability o...
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The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimizationalgorithms is particleswarmoptimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.
Query optimization is a Key research topic in the database research area aiming at solving the problem of premature convergence and local optimal trap in the traditional particle swarm optimization algorithm,this Pape...
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Query optimization is a Key research topic in the database research area aiming at solving the problem of premature convergence and local optimal trap in the traditional particle swarm optimization algorithm,this Paper Proposed a novel database query optimizationalgorithm called Hybrid Variable particleswarmoptimization (HV-PSO);Firstly,database query optimization mathematical model is established,then the optimal solution is found by using information transferring and sharing mechanism of *** research has two novelties which contribute to the literature: *** charge of particle inertia weight in the optimization process to accelerate the convergence;*** introduction of "hybrid" variation operator to increase the diversity of ***,the simulation experiments are carried out to test the performance of *** results show that the HV-PSO could solve the deficiency of the traditional particle swarm optimization algorithm,not only improving the database query efficiency,but also obtaining better query ***,it has predominant advantage for querying large relational connections.
The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data-driven models mainly focus on the prediction differences between algorithms, and there is relativel...
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The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data-driven models mainly focus on the prediction differences between algorithms, and there is relatively little analysis of the impact of different hyperparameters on prediction accuracy. Taking a 120 t converter in a Chinese steel plant as an example, this paper explores the application of particleswarmoptimization-back propagation neural network (PSO-BP) in converter temperature prediction. First, the Pauta criterion or Box plot method was used to preprocess the data by prescreening. Subsequently, the influence of the activation function, learning rate, and number of hidden layer nodes of BP on the prediction accuracy of the endpoint temperature were explored. Then we investigated the influence of PSO parameters on the optimal result of BP initial value. Comparing the temperature prediction hit rate before and after optimization, the BP model has hit rates of 63.64%, 79.22%, and 87.45% within +/- 10, +/- 15, and +/- 20 degrees C, respectively, and the PSO-BP model has hit rates of 68.40%, 84.85%, and 94.81%, respectively. In comparison, PSO-BP extracts data features more accurately, has higher stability, and has better accuracy in predicting the endpoint temperature of the converter. This article establishes a particleswarmoptimization-back propagation neural network (PSO-BP) model for predicting converter endpoint temperature, explores the influence of hyperparameters on the accuracy of PSO-BP prediction, reveals the principle of PSO for BP, and obtains the optimal parameter selection scheme for the model. Data validation confirms PSO-BP's effectiveness in extracting data features and achieving high prediction *** (c) 2024 WILEY-VCH GmbH
In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essenti...
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In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic particleswarmoptimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimizationalgorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.
High-precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural n...
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High-precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural network (particleswarmoptimization - backpropagation neural network - double extended Kalman filter [PSO-BPNN-DEKF]) are combined to estimate and correct the SOC of lithium-ion batteries, in which the initial weight and threshold of the BPNN are optimized by particle swarm optimization algorithm. Based on the second-order RC equivalent circuit model, parameter identification is carried out using the adaptive forgetting factor least squares (AFFRLS) method. Online parameter updates and SOC estimation are realized by DEKF algorithm. Then, the trained PSO-BPNN is used to predict the SOC estimation error in real time, and the SOC estimation value is corrected by adding prediction errors. The SOC estimates before and after correction under Beijing Dynamic Stress Test (BBDST), dynamic stress test (DST), and hybrid pulse power characterization (HPPC) were compared. Under BBDST, DST, and HPPC tests, the maximum errors of the corrected SOC estimates are 0.0107, 0.0090, and 0.0147, respectively. The root mean square error (RMSE) of the corrected SOC estimates decreased by 94.02%, 83.18%, and 88.03%, respectively, compared with the extended Kalman filtering (EKF). The mean absolute error (MAE) of the corrected SOC estimates remained around 0.1% for all the BBDST dynamic operating conditions at different temperatures. The experimental results demonstrate the accuracy, effectiveness, and temperature adaptability of the proposed algorithm for SOC estimation under complex conditions of lithium-ion batteries.
Since the coronavirus disease 2019 (COVID-19) pandemic, some patients with COVID-19 have experienced abnormal heart rates, posing potential health risks. In this study, we develop a noncontact method for measuring hea...
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Since the coronavirus disease 2019 (COVID-19) pandemic, some patients with COVID-19 have experienced abnormal heart rates, posing potential health risks. In this study, we develop a noncontact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in achieving accurate diagnosis and treatment. In this research, we collected data from 20 experimental testers based on palm images captured from photoplethysmography signals and measuring HR and HRV data by combining intelligent algorithms, namely, separation methods by particleswarmoptimization and independent component analysis signal. The proposed method's new contactless measurement performance can effectively eliminate infection concerns and obtain HR and HRV rapidly and handily. Moreover we provide higher accuracies for physiological parameters, namely, root mean square error (2.00 bpm), mean absolute percentage error (1.5%), and measurement time (8 s), than those in recently published literature.
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological ***,the complexity of porous media often limits the effectiveness of individual predic...
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Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological ***,the complexity of porous media often limits the effectiveness of individual prediction *** study introduces a novel particleswarmoptimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction ***,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock *** is observed that these methods exhibit computational biases in certain permeability *** PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their *** PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction *** outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.
This paper presents a sliding mode control based on particleswarmoptimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degra...
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This paper presents a sliding mode control based on particleswarmoptimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.
A mathematical model of electroslag remelting (ESR) process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID) controller...
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A mathematical model of electroslag remelting (ESR) process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID) controller tuned optimally by an improved particleswarmoptimization (IPSO) algorithm is proposed to control the two-input/two-output (TITO) ESR process. An adaptive chaotic migration mutation operator is used to tackle the particles trapped in the clustering field in order to enhance the diversity of the particles in the population, prevent premature convergence and improve the search efficiency of PSO algorithm. The simulation results show the feasibility and effectiveness of the proposed control method. The new method can overcome dynamic working conditions and coupling features of the system in a wide range, and it has strong robustness and adaptability.
The steady increase in energy demand results in under-voltage problems and an increase in the active power losses in electrical distribution networks. The optimal placement of capacitor banks in distribution systems i...
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The steady increase in energy demand results in under-voltage problems and an increase in the active power losses in electrical distribution networks. The optimal placement of capacitor banks in distribution systems is essential for enhancing the voltage profile and reducing power losses. This paper proposes a modified particleswarmoptimization (PSO) algorithm using an adaptive inertia constant to expand the search space and determine the optimal locations and sizes of capacitor banks. Two loss sensitivity indices (LSI) were applied to identify candidate buses. This methodology was applied to the IEEE 33-bus radial distribution system considering two scenarios: one with fixed capacitors (Case 1) and the other with switched capacitors (Case 2). The results demonstrate that the proposed algorithm effectively reduces system losses by 31.30% and 31.38% in Cases 1 and 2, respectively. In addition, in both cases, the voltage profiles were improved and maintained within allowable limits. Therefore, the proposed methodology is expected to work well in larger electrical distribution networks.
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