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Comprehensive Evaluation Method for Traffic Flow Data Quality Based on Grey Correlation Analysis and particle swarm optimization

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Journal of Systems Science and Systems Engineering 2024年第1期33卷 106-128页

作者： Wei Ba Baojun Chen Qi Li School of Automation and Electrical Engineering Dalian Jiaotong UniversityDalian116028China Faculty of Electronic Information and Electrical Engineering Dalian University of TechnologyDalian116023China

Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or ***,the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator,so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation *** order to evaluate the data more comprehensively,objectively and differentially,a novel comprehensive evaluation method based on particle swarm optimization(PSO)and grey correlation analysis(GCA)is presented in this *** first,an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution(TOPSIS)is ***,an objective function model of maximum difference of the comprehensive evaluation values is built,and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function ***,the performance of the proposed method is investigated through parameter analysis.A performance comparison of traffic flow data is carried out,and the simulation results show that the maximum average difference between the evaluation results and its mean value(MDR)of the proposed comprehensive evaluation method is 33.24%higher than that of TOPSIS-GCA,and 6.86%higher than that of *** proposed method has better differentiation than other methods,which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data,and the results more effectively reflect the differences in data quality,which will provide more effective data support for intelligent modeling,prediction and other applications.

关键词： Data quality comprehensive evaluation particle swarm optimization grey correlation analysis traffic flow data

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Planning of DC Electric Spring with particle swarm optimization and Elitist Non-dominated Sorting Genetic Algorithm

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 574-583页

作者： Qingsong Wang Siwei Li Hao Ding Ming Cheng Giuseppe Buja School of Electrical Engineering and Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment Southeast UniversityNanjing 210096China School of Software Engineering Southeast universitySuzhou 215123China

This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis.

关键词： DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source

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Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

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Journal of Mountain Science 2024年第2期21卷 397-412页

作者： ZHAO Haijun WEI Aihua MA Fengshan DAI Fenggang JIANG Yongbing LI Hui Key Laboratory of Shale Gas and Geoengineering Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Innovation Academy for Earth Science Chinese Academy of SciencesBeijing 100029China Hebei Province Key Laboratory of Sustained Utilization&Development of Water Recourse Hebei GEO UniversityShijiazhuang 050031China School of Water Resources and Environment Hebei GEO UniversityShijiazhuang 050031China The Sixth Geological Brigade of Geological Mineral Exploration and Development Bureau of Hebei Province Shijiazhuang 050085China

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility *** this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning *** total,133 historical debris flow records and 16 related factors were *** support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step ***,the particle swarm optimization(PSO)algorithm was employed to select the SVM model ***,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,*** statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the *** results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM ***,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing *** was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment ***,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning *** high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intens

关键词： Chengde Feature selection Support vector machine particle swarm optimization Principal component analysis Debris flow susceptibility

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Using Improved particle swarm optimization Algorithm for Location Problem of Drone Logistics Hub

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Computers, Materials & Continua 2024年第1期78卷 935-957页

作者： Li Zheng Gang Xu Wenbin Chen School of Mathematics and Computer Sciences Nanchang UniversityNanchang330031China

Drone logistics is a novel method of distribution that will become *** advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation *** firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery *** simulate the drone logistics environment,this study presents a new mathematical *** model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub ***,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this *** DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed ***,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the *** maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current *** DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location *** the results show the location of the drone logistics hub is solved by DHPSO effectively.

关键词： Drone logistics location problem mathematical model diversity particle swarm optimization

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Determination of the Pile Drivability Using Random Forest Optimized by particle swarm optimization and Bayesian Optimizer

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2024年第1期141卷 871-892页

作者： Cheng, Shengdong Gao, Juncheng Qi, Hongning Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg Xian 710048 Peoples R China Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China

Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian optimization (BO) and particle swarm optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles (i.e., maximum compressive stress, maximum tensile stress, and blow per foot). In addition, support vector regression, extreme gradient boosting, k nearest neighbor, and decision tree are also used and applied for comparison purposes. In order to train and test these models, among the 4072 datasets collected with 17 model inputs, 3258 datasets were randomly selected for training, and the remaining 814 datasets were used for model testing. Lastly, the results of these models were compared and evaluated using two performance indices, i.e., the root mean square error (RMSE) and the coefficient of determination (R2). The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters, specifically 0.044, 0.438, and 0.146; and higher R2 values than other implemented techniques, specifically 0.966, 0.884, and 0.977. In addition, the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo (MC) simulation. It can be concluded that the optimized RF model could be used to predict the performance of the pile, and it may provide a useful reference for solving some problems under similar engineering conditions.

关键词： Random forest regression model pile drivability Bayesian optimization particle swarm optimization PREDICTION CAPACITY MODELS

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particle swarm optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

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Journal of Computer and Communications 2024年第3期12卷 160-183页

作者： Hend S. Salem Mohamed A. Mead Ghada S. El-Taweel Department of Computer Sciences Suez Canal University Ismailia Egypt

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the particle swarm optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

关键词： Big COVID-19 Data Machine Learning Hyperparameter optimization particle swarm optimization Computational Intelligence

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A New Multiobjective particle swarm optimization Using Local Displacement and Local Guides

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Open Journal of optimization 2024年第2期13卷 31-49页

作者： Saïd Charriffaini Rawhoudine Abdoulhafar Halassi Bacar Laboratoire des Mathmatiques Statistiques Informatique et Applications (LMSIA) Dpartement des Mathmatiques Physique-Chimie et Informatique Facult des Sciences et Techniques Universit des Comores Moroni Comoros

This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.

关键词： particle swarm optimization Multiobjective optimization Attractor-Based Displacement Square Root Distance Crowding Distance

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particle swarm optimization for Solving Sine-Gordan Equation

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Computer Systems Science & Engineering 2023年第6期45卷 2647-2658页

作者： Geeta Arora Pinkey Chauhan Muhammad Imran Asjad Varun Joshi Homan Emadifar Fahd Jarad Lovely Professional University Punjab144118India Jaypee Institute of Information Technology Noida201304India Department of Mathematics University of Management and TechnologyLahore54770Pakistan Department of Mathematics Hamedan BranchIslamic Azad UniversityHamedanIran Department of Mathematics Cankaya UniversityEtimesgutAnkara06790Turkey Department of Mathematics King Abdulaziz UniversitySaudi Arabia Department of Medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwan

The term‘optimization’refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable *** majority of real-world situations can be modelled as an optimization *** complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization *** swarm optimization is a potential global optimization technique that has been widely used to address problems in a variety of *** idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation,whose numerical solutions show the soliton form and has diverse *** implemented optimization technique is employed to determine the involved parameter in the basis functions,which was previously approximated as a random number in the work reported till now in the *** obtained results are comparable with the results obtained in the *** work is presented in the form of figures and tables and is found encouraging.

关键词： Differential quadrature method B-spline particle swarm optimization Sine-Gordan equation

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Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm

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Chinese Journal of Chemical Engineering 2023年第6期58卷 244-255页

作者： Danlei Chen Yiqing Luo Xigang Yuan Chemical Engineering Research Center School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300350China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300350China

Cascade refrigeration system(CRS)can meet a wider range of refrigeration temperature requirements and is more energy efficient than single-refrigerant refrigeration system,making it more widely used in low-temperature industry *** synthesis of a CRS with simultaneous consideration of heat integration between refrigerant and process streams is challenging but promising for significant cost saving and reduction of carbon *** study presented a stochastic optimization method for the synthesis of *** MINLP model was formulated based on the superstructure developed for the CRS,and an optimization framework was proposed,where simulated annealing algorithm was used to evolve the numbers of pressure/temperature levels for all sub-refrigeration systems,and particle swarm optimization algorithm was employed to optimize the continuous *** effectiveness of the proposed methodology was verified by a case study of CRS optimization in an ethylene plant with 21.89%the total annual cost saving.

关键词： Optimal design Process systems particle swarm optimization Simulated annealing Mathematical modeling

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Crack detection of the cantilever beam using new triple hybrid algorithms based on particle swarm optimization

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Frontiers of Structural and Civil Engineering 2022年第9期16卷 1127-1140页

作者： Amin GHANNADIASL Saeedeh GHAEMIFARD Department of Civil Engineering University of Mohaghegh ArdabiliArdabil 56199-11367Iran

The presence of cracks in a concrete structure reduces its performance and increases in the size of cracks result in the failure of the ***,the accurate determination of crack characteristics,such as location and depth,is one of the key engineering issues for assessment of the reliability of *** paper deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on particle swarm optimization(PSO);these hybrids are particle swarm optimization-Genetic Algorithm-Firefly Algorithm(PSO-GA-FA),particle swarm optimization-Grey Wolf optimization-Firefly Algorithm(PSO-GWO-FA),and particle swarm optimization-Genetic Algorithm-Grey Wolf optimization(PSO-GA-GWO).A strong correlation exists between the changes in the natural frequency of a concrete beam and the crack ***,the location and depth of a crack in a beam can be predicted by measuring its natural ***,the measured natural frequency can be used as the input parameter of the *** this paper,this is applied to identify crack location and depth in a cantilever beam using the new hybrid *** results show that among the proposed triple hybrid algorithms,the PSO-GA-FA and PSO-GWO-FA algorithms are much more effective than PSO-GA-GWO algorithm for the crack detection.

关键词： crack cantilever beam triple hybrid algorithms particle swarm optimization

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