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The Influence of particle swarm optimization-Back Propagation Neural Network Hyperparameter Selection on the Prediction Accuracy of Converter Endpoint Temperature

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STEEL RESEARCH INTERNATIONAL 2024年第10期95卷

作者： Xin, Tongze Wang, Min Li, Yihong Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 100083 Peoples R China Univ Sci & Technol Beijing Tech Support Ctr Prevent & Control Disastrous Acci State Key Lab Adv Met Beijing 100083 Peoples R China Taiyuan Univ Sci & Technol Coll Mat Sci & Engn Taiyuan 030024 Peoples R China

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 particle swarm optimization-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 particle swarm optimization-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

关键词： back propagation neural network converter endpoint prediction data driven hyperparameter analysis particle swarm optimization algorithm

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Fitness Sharing Chaotic particle swarm optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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Computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

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 particle swarm optimization(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 optimization algorithms,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.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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High-precision state of charge estimation of lithium-ion batteries based on improved particle swarm optimization-backpropagation neural network-dual extended Kalman filtering

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2024年第3期52卷 1192-1209页

作者： Chen, Lu Wang, Shunli Chen, Lei Qiao, Jialu Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang Peoples R China Sichuan Univ Coll Elect Engn Chengdu Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China

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 (particle swarm optimization - 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.

关键词： backpropagation neural network double extended Kalman filter estimation of state of charge particle swarm optimization algorithm ternary lithium battery

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Enhanced permeability prediction in porous media using particle swarm optimization with multi-source integration

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Artificial Intelligence in Geosciences 2024年第1期5卷 282-293页

作者： Zhiping Chen Jia Zhang Daren Zhang Xiaolin Chang Wei Zhou State Key Laboratory of Water Resources Engineering and Management Wuhan UniversityWuhan 430072China Institute of Water Engineering Sciences Wuhan UniversityWuhan 430072China Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering of Ministry of Education Wuhan UniversityWuhan 430072China

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 particle swarm optimization-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.

关键词： Porous media particle swarm optimization algorithm Multi-source data integration Permeability prediction

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Sliding mode control based on particle swarm optimization neural network and adaptive reaching law

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第4期46卷 741-751页

作者： Chen, Jiqing Zhang, Haiyan Pan, Shangtao Tang, Qingsong Guangxi Univ Sch Mech Engn Nanning 530005 Peoples R China

This paper presents a sliding mode control based on particle swarm optimization 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.

关键词： Manipulators sliding mode control RBFNN particle swarm optimization algorithm

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Multivariable PID Decoupling Control Method of Electroslag Remelting Process Based on Improved particle swarm optimization (PSO) algorithm

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INFORMATION 2014年第1期5卷 120-133页

作者： Wang, Jie-Sheng Ning, Chen-Xu Yang, Yang Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Liaoning Peoples R China

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 particle swarm optimization (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.

关键词： electroslag remelting process multivariable system PID controller particle swarm optimization algorithm

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A new particle swarm optimization-enhanced deep neural network for automatic ECG arrhythmias classification

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2025年 1-15页

作者： Kahlessenane, Yaaqoub Bouaziz, Fatiha Siarry, Patrick Jijel Univ Elect Dept BP 98 Ouled Aissa Jije 18000 Algeria Jijel Univ Mechatron Lab LMT BP 98 Ouled Aissa Jijel 18000 Algeria Paris East Creteil Univ Lab Images Signals & Intelligent Syst LISSI Paris France

This study proposes an ECG classification system using particle swarm optimization (PSO) for automated deep neural network hyperparameter tuning. PSO optimizes five key parameters: neuron counts in two fully connected layers, dropout rate, learning rate, and optimizer selection. ECG signals undergo wavelet decomposition for feature extraction, with classification performed on the MIT-BIH Arrhythmia Database across five heartbeat classes. The PSO-optimized model achieves superior performance with 99.76% accuracy, 99.34% precision, and 99.21% F1 score, demonstrating PSO's effectiveness in improving model reliability while reducing manual effort.

关键词： Classification approach deep neural network particle swarm optimization algorithm feature extraction hyperparameters

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Data dissemination protocol for VANETs to optimize the routing path using hybrid particle swarm optimization with sequential variable neighbourhood search

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TELECOMMUNICATION SYSTEMS 2023年第2期84卷 153-165页

作者： Gopalan, S. Harihara Ashok, J. Manikandan, A. Ramalingam, S. Sri Ramakrishna Engn Coll Coimbatore India VSB Engn Coll Dept Elect & Commun Kaur India SSM Inst Engn & Technol Dept Elect & Commun Engn Dindugal 624002 India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore 642109 India

A vehicular Ad-Hoc Network (VANET) is a form of Mobile Ad-Hoc Network (MANET) which employs wireless routers that are inside every vehicle to operate as a node. The process of data dissemination is used to improve the quality of travel to avoid unnecessary accidents in VANET. Many legacy protocols use this type of messaging activity to ensure fair road safety without concern for network congestion. Node congestion increases with control of routing overhead packets. Therefore, this paper proposes a Data Dissemination Protocol (DDP). VANET routing protocols can be divided into two categories: topology-based routing protocols and location-based routing protocols. The goal is to relay emergency signals to stationary nodes as soon as possible. The standard messages will be routed to the FIFO queue. Multiple routes were found using the Time delay-based Multipath Routing (TMR) approach to transmit these messages to a destination node, and particle swarm Optimisation (PSO) is utilized to find the optimal and secure path. Sequential Variable Neighborhood Search (SVNS) algorithm is applied in order to optimize the particles' position with Local Best particle and Global Best particle (LBGB). The proposed method PSO-SVNS-LBGB is compared with different methods such as PSO-SVNS-GB, PSO-SVNS-LB, PSO-SVNS-CLB, PSO-SVNS-CGB. The experimental results show significant improvements in throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and energy consumption. The simulation environment was conducted in NS2.34 is preferred for network simulation, and the VANET simulator used is SUMO and MOVE software. With a 98.41 ms delay and an average speed of 60 km/h, the PSO-SVNS-LBGB approach is suggested.

关键词： Vehicular ad-hoc networks (VANETs) Bio-inspired routing Meta-heuristics particle swarm optimization algorithm Sequential variable neighbourhood search

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Global-guidance chaotic multi-objective particle swarm optimization method for pneumatic suspension handling and ride quality enhancement on the basis of a thermodynamic model of a full vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2023年第14期237卷 3334-3352页

作者： Ghorbany, Mohammad Ebrahimi-Nejad, Salman Mollajafari, Morteza Iran Univ Sci & Technol Sch Automot Engn Vehicle Dynam Syst Res Lab Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Vehicle Elect & Elect Res Lab Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Vehicle Elect & Elect Res Lab Farjam StHengam StResalat Sq Tehran *** Iran

In this paper, the parameters of the validated suspension system model of a full-vehicle are tuned through design sensitivity analyses, and then Multi-Objective particle swarm optimization (MOPSO) is used to enhance vehicle ride comfort, which is the vertical whole-body vibrations, and handling features, that is, roll motion and road holding, simultaneously. The parameters of this thermodynamic-based pneumatic suspension system model are comprised of the air spring reservoir volume, orifice resistance, initial volume, and pressure of the pneumatic springs. To enhance the convergence rate, computational times, and diversity of the swarm particles, we have incorporated chaotic dynamics into the MOPSO using the Logistic Map chaotic method to initialize the population and also employed the leader-based global guidance techniques to conduct the potential solutions in each iteration. The analysis of the proposed modeling and optimization results show that the suspension system has been reasonably boosted in terms of vehicle handling and ride comfort. Quantitatively, the RMS acceleration and pitch angle has been reduced by about 71% and 57%, respectively, showing a substantial improvement in passenger comfort. Furthermore, the proposed approach caused an increase in tire road-holding force by about 148% and a reduction of roll angle by 33% which results in an enhancement in vehicle handling, boosting vehicle driving safety.

关键词： Multi-objective optimization pneumatic springs particle swarm optimization algorithm optimal vehicle dynamics performance ride quality handling

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Hardware implementation of particle swarm optimization with chaotic fractional-order

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NEURAL COMPUTING & APPLICATIONS 2023年第15期35卷 11249-11268页

作者： Zermani, Aymen Manita, Ghaith Feki, Elyes Mami, Abdelkader Tunis El Manar Univ LAPER Dept Phys Fac Sci Campus Univ El Manar Tunis 2092 Tunisia Univ Sousse Lab MARS ISITCom LR17ES05 Sousse Tunisia

Engineering optimization methods based on meta-heuristic algorithms are computationally demanding and might present complex implementation issues for embedded devices with high constraints. One of the interesting meta-heuristic methods frequently utilized to address diverse optimization issues in the real world is particle swarm optimization (PSO). However, the most challenging constraints in real applications;having an optimal execution time for an optimal solution, added to avoid premature convergence and stagnation in the local optima. In this study, we propose the hardware implementation of an improved particle swarm optimization algorithm by using the fractional order concept and chaos theory named CF-PSO. The concept is to substitute a chaotic sequence generator for the values of random variables and to use fractional calculus in the velocity and position expressions. We provide a hardware design of the enhanced PSO on Field Programmable Gate Array (FPGA) to obtain substantially quicker execution speeds than those attainable in software implementation. A Xilinx Virtex-7 Pro Development Kit is used to implement the hardware-improved PSO design and evaluate its performance. To start, we put the suggested architecture to the test using a benchmark of four functions to demonstrate the effectiveness of this hardware implementation. Secondly, we contrasted it with three other PSO variations: the conventional PSO, the PSO using fractional order velocity (F-V-PSO), and the PSO using fractional order velocity and position (F-VP-PSO). The experimental results show that the hardware implementation of F-VP-PSO is between 1.082 and 21.25 times faster. CFPSO algorithm can be implemented in the Virtex7 VC707 evaluation platform by utilizing less than 2% of the slice 462 registers and 16% of the slice LUT (Look up Table) resources.

关键词： particle swarm optimization algorithm Fractional-order difference Chaos theory FPGA Hardware

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