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Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

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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Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

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Computers, Materials & Continua 2024年第2期78卷 1687-1711页

作者： Xin Fan Shuqing Zhang Kaisheng Wu Wei Zheng Yu Ge School of Software Nanchang Hangkong UniversityNanchang330063China Software Testing and Evaluation Center Nanchang Hangkong UniversityNanchang330063China

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target ***,existing CPDP methods only consider linear correlations between features(indicators)of the source and target *** models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target *** a result,the performance of such CPDP models is *** this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as *** Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the *** redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss ***,cross-project defect prediction is achieved through the application of the SMOTE data sampling *** Under Curve(AUC)and F1 scores(F1)are used as evaluation *** paper conducted experiments on 27 projects from four public datasets to validate the proposed *** results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 *** indicates that the proposed method exhibits favorable performance characteristics.

关键词： Cross-project defect prediction deep canonical correlation analysis feature similarity

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Software Defect Prediction Method Based on Stable Learning

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Computers, Materials & Continua 2024年第1期78卷 65-84页

作者： Xin Fan Jingen Mao Liangjue Lian Li Yu Wei Zheng Yun Ge College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210012China School of Software Nanchang Hangkong UniversityNanchang330029China Software Testing and Evaluation Center Nanchang Hangkong UniversityNanchang330029China

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and *** previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data ***,target projects often lack sufficient data,which affects the performance of the transfer learning *** addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning *** address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual *** method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code *** the model training process,target project data are not required as prior *** the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the *** approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction *** evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE *** experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction ***,SDP-SL can effectively enhance within-and cross-project defect predictions.

关键词： Software defect prediction code visualization stable learning sample reweight residual network

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Gyroscope Dynamic Balance Counterweight Prediction Based on Multi-Head ResGAT Networks

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Computer Modeling in Engineering & sciences 2024年第6期139卷 2525-2555页

作者： Wuyang Fan Shisheng Zhong School of Mechanical Engineering Harbin Institute of TechnologyHarbin150000China

The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided *** dynamic balance debugging,reliance on rudimentary counterweight empirical formulas persists,resulting in suboptimal debugging accuracy and an increased repetition *** mitigate this challenge,we present a multi-head residual graph attention network(ResGAT)model,designed to predict dynamic balance counterweights with high *** this research,we employ graph neural networks for interaction feature extraction from assembly graph *** SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model,which is capable of predicting gyroscope counterweights under small-sample *** results of our experiments demonstrate the effectiveness of the proposed approach in predicting dynamic gyroscope counterweight in its assembly *** approach surpasses current methods in mitigating repetition rates and enhancing the assembly efficiency of gyroscopes.

关键词： Gyroscope counterweight assembly small-sample ResGAT repetition rate

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Performance Prediction Based Workload Scheduling in Co-Located Cluster

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Computer Modeling in Engineering & sciences 2024年第5期139卷 2043-2067页

作者： Dongyang Ou Yongjian Ren Congfeng Jiang School of Computer science and Technology Hangzhou Dianzi UniversityHangzhou310018China

Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource *** to resource competition between batch jobs and online services,co-location frequently impairs the performance of online *** study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for *** performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on ***-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch *** the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling *** with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on *** show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.

关键词： Co-located cluster workload scheduling online service batch jobs data center

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Digitalizing river aquatic ecosystems

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Journal of Environmental sciences 2024年第3期137卷 677-680页

作者： Yaohui Bai Hui Lin Chenchen Wang Qiaojuan Wang Jiuhui Qu Key Laboratory of Drinking Water science and Technology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China University of Chinese Academy of sciences Beijing 100049China School of Environmental and Municipal Engineering Tianjin Chengjian UniversityTianjin 300384China Center for Water and Ecology Tsinghua UniversityBeijing 100084China

Traditional river health assessment relies on limited water quality indices and representative organism activity,but does not comprehensively obtain biotic and abiotic information of the ***,we propose a new approach to evaluate the ecological and health risks of river aquatic ***,detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination(especially emerging pollutants)and DNA/RNA ***,supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and *** proposed methodology transforms river ecosystem health assessment and can be applied in river management.

关键词： Digitalizing River ecosystem health Emerging pollutants High throughput sequencing Machine learning

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Automatic SOC Equalization Strategy of Energy Storage Units with DC Microgrid Bus Voltage Support

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Energy Engineering 2024年第2期121卷 439-459页

作者： Jingjing Tian Shenglin Mo Feng Zhao Xiaoqiang Chen School of Automation&Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China Key Laboratory of Opto-Technology and Intelligent Control(Lanzhou Jiaotong University) Ministry of EducationLanzhou730070China

In this paper,an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load and PV power variations in a stand-alone DC *** strategy includes primary and secondary *** them,the primary control suppresses the DC microgrid voltage fluctuation through the I and II section control,and the secondary control aims to correct the P-U curve of the energy storage system and the PV system,thus reducing the steady-state bus voltage *** simulation results demonstrate that the proposed control strategy effectively achieves SOC balancing and enhances the immunity of bus *** proposed strategy improves the voltage fluctuation suppression ability by approximately 39.4%and 43.1%under the PV power and load power fluctuation conditions,***,the steady-state deviation of the bus voltage,△U_(dc) is only 0.01–0.1 V,ensuring stable operation of the DC microgrid in fluctuating power environments.

关键词： Automatic equalization independent DC microgrid improve droop control secondary control state of charge

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Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

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Computer Modeling in Engineering & sciences 2024年第6期139卷 3147-3165页

作者： Yifan Huang Zikang Zhou Mingyu Li Xuedong Luo Faculty of Engineering China University of GeosciencesWuhan430074China Institute of Geological Survey China University of GeosciencesWuhan430074China

Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration *** this study,Tuna Swarm Optimization(TSO),Whale Optimization Algorithm(WOA),and Cuckoo Search(CS)were used to optimize two hyperparameters in support vector regression(SVR).Based on these methods,three hybrid models to predict peak particle velocity(PPV)for bench blasting were ***-eight samples were collected to establish the PPV database,eight initial blasting parameters were chosen as input parameters for the predictionmodel,and the PPV was the output *** predictive performance evaluation indicators,the coefficient of determination(R2),rootmean square error(RMSE),mean absolute error(MAE),and a10-index were *** normalizedmutual information value is then used to evaluate the impact of various input parameters on the PPV prediction *** to the research findings,TSO,WOA,and CS can all enhance the predictive performance of the SVR *** TSO-SVR model provides the most accurate *** performances of the optimized hybrid SVR models are superior to the unoptimized traditional prediction *** maximum charge per delay impacts the PPV prediction value the most.

关键词： Blasting vibration metaheuristic algorithms support vector regression peak particle velocity normalized mutual information

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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Computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information science Hubei Engineering UniversityXiaogan432000China School of Computer science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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Research and Application of a Multi-Field Co-Simulation Data Extraction Method Based on Adaptive Infinitesimal Element

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工程与科学中的计算机建模(英文) 2024年第1期138卷 321-348页

作者： Changfu Wan Wenqiang Li Sitong Ling Yingdong Liu Jiahao Chen School of Mechanical Engineering Sichuan UniversityChengdu610065China Innovation Method and Creative Design Key Laboratory of Sichuan Province Sichuan UniversityChengdu610065China

Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of *** this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is *** co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data *** on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step *** multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering *** with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction ***,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.

关键词： Multi-field co-simulation adaptive infinitesimal elements principal component analysis fireworks algorithm sintering furnace simulation

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