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A Proposed Feature Selection particle swarm optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

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Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

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 optimization algorithms is particle swarm optimization(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.

关键词： optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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Database Query optimization based on Hybrid Variable particle swarm optimization algorithm

Database Query Optimization based on Hybrid Variable Particl...

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2014年国际计算机科学与软件工程学术会议

作者： Fen CHEN Yan TANG Fan-Ping ZHANG Suqian College Department of Computer Science SuqianJiangsuChina Computer and Information Engineering Hohai UniversityNanjingJiangsuChian College of Water Conservancy and Hydropower Engineering Hohai UniversityJiangsuChina

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 optimization algorithm called Hybrid Variable particle swarm optimization (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.

关键词： Database query particle swarm optimization algorithm Dynamic inertial weigh Multi-Joint Query Hybrid mutation Query plan

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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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Contactless Heart Rate and Heart Rate Variability Estimation Using Palm Images with particle swarm optimization and Independent Component Analysis algorithms

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SENSORS AND MATERIALS 2024年第11期36卷 4757-4774页

作者： Su, Te-Jen Lin, Wei-Hong Yang, Wen-Rong Hung, Ya-Chung Zhuang, Qian-Yi Wang, Shih-Ming Tseng, Li-chin Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung 80782 Taiwan Cheng Shiu Univ Dept Comp Sci & Informat Engn Kaohsiung 833301 Taiwan

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 particle swarm optimization 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.

关键词： particle swarm optimization algorithm heart rate heart rate variability independent component analysis contactless measurement

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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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particle swarm optimization using Adaptive Inertia Weight for Optimal Capacitor Bank Placement in Radial Distribution Networks

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第11期38卷 2675-2684页

作者： Castillo, C. A. Lopez Tecsi, A. V. Huillcahuaman Gastelu, J. Villavicencio Obispo, A. E. Univ Tecnol Peru Lima Peru

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 particle swarm optimization (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.

关键词： Electrical Distribution Networks Capacitor Banks particle swarm optimization algorithm Under Voltage Power Losses

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