期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到1,953篇文章
< 1 2 … 98 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Structural Damage Diagnosis Based onMulti-Stage Sparrow Search Algorithm 认领 引用
1
作者 Lijun Yang Qiuwei Yang 《Computers, Materials & Continua》 SCIE EI 2026年第9期2449-2468,共20页
This study proposes a Multi-Stage Sparrow Search Algorithm(MS-SSA)for precise structural damage identification.Initially,the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-W... This study proposes a Multi-Stage Sparrow Search Algorithm(MS-SSA)for precise structural damage identification.Initially,the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-Woodbury formula,and an objective function is constructed by integrating the sensitivity equations with the L2-norm penalty.Subsequently,MS-SSA is implemented to sequentially achieve preliminary damage localization and accurate quantification.In the localization phase,a constrained narrow-bound search space is predefined to identify potential damage regions.Leveraging this feedback,the sensitivity equations are condensed,and the search boundaries are adaptively refined for the quantification phase,where SSA is reapplied to precisely determine damage severity while mitigating misjudgments.TheMS-SSA framework exhibits two distinct advantages:(i)Phase I localization accelerates convergence by constraining the search space,as it does not target precise quantification;and(ii)the significant reduction in unknowns achieved by excluding intact elements in Phase II enables rapid convergence to the global optimum.Comparative studies against the GreyWolf Optimizer(GWO),Whale Optimization Algorithm(WOA),and standard SSA demonstrate that the proposed method effectively overcomes computational instability,slowconvergence,and large errors inherent in swarm intelligence optimization for damage identification.Specifically,numerical case studies reveal that the identification error is reduced to merely 9%~22%of that associated with existing methods,with experimental validation confirming reductions to 18%~22%.Overall,the proposed approach achieves high-fidelity damage identification while eliminating false positives and false negatives. 展开更多
关键词 Damage diagnosis static displacement sensitivity Sparrow Search Algorithm(SSA) narrow search range
暂未订购 下载PDF
Residual Strength Prediction of Corroded Pipelines Based on Sparrow Search Algorithm-Optimized Kernel Extreme Learning Machine 认领 引用
2
作者 Zixuan Zong Tingting Long +3 位作者 Huaqing Dong Guoqiang Huang Xiao Meng Mohammadamin Azimi 《Structural Durability & Health Monitoring》 EI 2026年第3期480-495,共16页
This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KEL... This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KELM model addresses the limitations of traditional evaluation methods and single machine learning models in residual strength prediction.A dataset comprising 80 samples from burst tests and finite element simulations was used to validate the model.Results demonstrate that the SSA-KELM model achieves superior prediction accuracy with a maximum relative error of 13.54%and minimum relative error of 0.20%.The model’s mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are 0.658%,0.780%,and 4.38%,respectively,significantly outperforming conventional machine learning models and traditional assessment methods.This research provides a reliable tool for evaluating pipeline integrity and maintenance planning. 展开更多
关键词 Pipeline corrosion residual strength prediction kernel extreme learning machine sparrow search algorithm machine learning
暂未订购 下载PDF
An NOMA-VLC power allocation scheme for multi-user based on sparrow search algorithm 认领 引用 被引量：1
3
作者 WANG Xing WANG Haitao +3 位作者 DONG Zhenliang XIONG Yingfei SHI Huili WANG Ping 《Optoelectronics Letters》 EI 2025年第5期278-283,共6页
A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the pote... A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the potential fairness issue that may arise from the maximum sum-rate based objective function and the optical power constraints are set considering the non-negativity of the transmit signal, the requirement of the human eyes safety and all users' quality of service(Qo S). Then, the SSA is utilized to solve this optimization problem. Moreover, to demonstrate the superiority of the proposed strategy, it is compared with the fixed power allocation(FPA) and the gain ratio power allocation(GRPA) schemes. Results show that regardless of the number of users considered, the sum-rate achieved by SSA consistently outperforms that of FPA and GRPA schemes. Specifically, compared to FPA and GRPA schemes, the sum-rate obtained by SSA is increased by 40.45% and 53.44% when the number of users is 7, respectively. The proposed SSA also has better performance in terms of user fairness. This work will benefit the design and development of the NOMA-visible light communication(VLC) systems. 展开更多
关键词 NOMA logarithmic utility function VLC Sparrow Search Algorithm sparrow search algorithm ssa fairness issue power allocation Sum Rate
暂未订购 下载PDF
Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network 认领 引用 被引量：3
4
作者 Yu Zhang Daoyu Zhang TiezhouWu 《Energy Engineering》 EI 2025年第1期203-220,共18页
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr... Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%. 展开更多
关键词 Lithium-ion battery state of health differential thermal voltammetry Sparrow Search Algorithm
暂未订购 下载PDF
Improved sparrow search algorithm for inversion of geometric parameters of earthquake source faults 认领 引用
5
作者 Leyang Wang Xuekai Zhou +2 位作者 Zhanglin Sun Can Xi Hao Xiao 《Geodesy and Geodynamics》 EI CSCD 2025年第6期665-680,共16页
With the continuous improvement of the accuracy of geodetic deformation data,the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms.In this research,an improved Spar... With the continuous improvement of the accuracy of geodetic deformation data,the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms.In this research,an improved Sparrow Search Algorithm(SSA)is proposed for the seismic source parameter inversion problem.By replacing the original population generation in the improved algorithm with Latin hypercubic sampling,the Sparrow Search Algorithm reduces the repetition of samples in the population initialization.Subsequently,the algorithm introduces adaptive weights in the discoverer generation phase of the sparrow algorithm and combines the Levy flight strategy to make the algorithm more comprehensive and improve the search accuracy during the whole iteration process.Therefore,the improved Latin hypercube-based sparrow search algorithm(ILHSSA)has better advantages in terms of iterative convergence speed and stability.In order to verify the performance of ILHSSA,the basic genetic algorithm(GA)and sparrow search algorithm(SSA)are examined and compared with ILHSSA by simulated earthquakes of two different earthquake types.The simulation experiments show that the improved algorithm ILHSSA outperforms SSA in accuracy and stability.Compared with the GA algorithm,ILHSSA can achieve the same inversion accuracy as GA,and it even surpasses GA in inversion speed and the inversion results of some parameters,demonstrating better stability.Finally,the improved algorithm is used for the 2017 Bodrum-Cos earthquake and the 2016 Amatrice earthquake in Italy.The inversion results all reflect the practicality and reliability of the improved algorithm. 展开更多
关键词 Sparrow search algorithm Latin hypercube Source parameter inversion Bodrum-Coase earthquake Amatrice earthquake
暂未订购 下载PDF
Optimized control of grid-connected photovoltaic systems:Robust PI controller based on sparrow search algorithm for smart microgrid application 认领 引用
6
作者 Youssef Akarne Ahmed Essadki +2 位作者 Tamou Nasser Maha Annoukoubi Ssadik Charadi 《Global Energy Interconnection》 EI CSCD 2025年第4期523-536,共14页
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi... The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems. 展开更多
关键词 Smart microgrid Photovoltaic system PI controller Sparrow search algorithm Grid-connected Metaheuristic optimization
暂未订购 下载PDF
A Clustering Model Based on Density Peak Clustering and the Sparrow Search Algorithm for VANETs 认领 引用
7
作者 Chaoliang Wang Qi Fu Zhaohui Li 《Computers, Materials & Continua》 SCIE EI 2025年第8期3707-3729,共23页
Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead... Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios. 展开更多
关键词 VANETs cluster density peak clustering sparrow search algorithm
暂未订购 下载PDF
NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization 认领 引用
8
作者 Hui Lv Yuer Yang Yifeng Lin 《Computers, Materials & Continua》 SCIE EI 2025年第10期925-953,共29页
It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional ... It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional Sparrow Search Algorithm(SSA)suffers from limited global search capability,insufficient population diversity,and slow convergence,which often leads to premature stagnation in local optima.Despite the proposal of various enhanced versions,the effective balancing of exploration and exploitation remains an unsolved challenge.To address the previously mentioned problems,this study proposes a multi-strategy collaborative improved SSA,which systematically integrates four complementary strategies:(1)the Northern Goshawk Optimization(NGO)mechanism enhances global exploration through guided prey-attacking dynamics;(2)an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom;(3)a dual chaotic initialization method(Bernoulli and Sinusoidal maps)increases population diversity and distribution uniformity;and(4)an elite retention strategy maintains solution quality and prevents degradation during iterations.These strategies cooperate synergistically,forming a tightly coupled optimization framework that significantly improves search efficiency and robustness.Therefore,this paper names it NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization.Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude.Compared with SSA,GWO,ISSA,and CSSOA,NTSSA improves solution accuracy by up to 14.3%(F8)and 99.8%(F12),while accelerating convergence by approximately 1.5–2×.The Wilcoxon rank-sum test(p<0.05)indicates that NTSSA demonstrates a statistically substantial performance advantage.Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation,chaos-based diversification,and elite preservation ensures both high convergence accuracy and global stability.This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation,offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design. 展开更多
关键词 Sparrow search algorithm multi-strategy fusion t-distribution elite retention strategy wilcoxon rank-sum test
暂未订购 下载PDF
A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems 认领 引用 被引量：15
9
作者 Andi Tang Huan Zhou +1 位作者 Tong Han Lei Xie 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第1期331-364,共34页
The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence spe... The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms. 展开更多
关键词 Sparrow search algorithm global optimization adaptive step benchmark function chaos map
暂未订购 下载PDF
Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm 认领 引用 被引量：5
10
作者 Xiaoge Wei Yuming Zhang +2 位作者 Huaitao Song Hengjie Qin Guanjun Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1295-1316,共22页
Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Fi... Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential. 展开更多
关键词 Sparrow search algorithm optimization and improvement function test set evacuation path planning
暂未订购 下载PDF
The Chaos Sparrow Search Algorithm:Multi-layer and Multi-pass Welding Robot Trajectory Optimization for Medium and Thick Plates 认领 引用 被引量：3
11
作者 Song Mu Jianyong Wang Chunyang Mu 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第5期2602-2618,共17页
The welding of medium and thick plates has a wide range of applications in the engineering field.Industrial welding robots are gradually replacing traditional welding operations due to their significant advantages,suc... The welding of medium and thick plates has a wide range of applications in the engineering field.Industrial welding robots are gradually replacing traditional welding operations due to their significant advantages,such as high welding quality,high work efficiency,and effective reduction of labor intensity.Ensuring the accuracy of the welding trajectory for the welding robot is crucial for guaranteeing welding quality.In this paper,the author uses the chaos sparrow search algorithm to optimize the trajectory of a multi-layer and multi-pass welding robot for medium and thick plates.Firstly,the Sparrow Search Algorithm(SSA)is improved by introducing tent chaotic mapping and Gaussian mutation of the inertia weight factor.Secondly,in order to prevent the welding robot arm from colliding with obstacles in the welding environment during the welding process,maintain the stability of the welding robot,and ensure the continuous stability of the changes in each joint angle,joint angular velocity,and angular velocity of the joint angle,a welding robot model is established by improving the Denavit-Hartenberg parameter method.A multi-objective optimization fitness function is used to optimize the trajectory of the welding robot,minimizing time and energy consumption.Thirdly,the optimization and convergence performance of SSA and Chaos Sparrow Search Algorithm(CSSA)are compared through 10 benchmark test functions.Based on the six sets of test functions,the CSSA algorithm consistently maintains superior optimization performance and has excellent stability,with a faster decline in the convergence curve compared to the SSA algorithm.Finally,the accuracy of welding is tested through V-shaped multi-layer and multi-pass welding experiments.The experimental results show that the CSSA algorithm has a strong superiority in trajectory optimization of multi-layer and multi-pass welding for medium and thick plates,with an accuracy rate of 99.5%.It is an effective optimization method that can meet the actual needs of production. 展开更多
关键词 Medium and thick plates The Chaos Sparrow Search Algorithm Welding robot Tent chaotic mapping Denavit-Hartenberg Trajectory optimization
Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China 认领 引用 被引量：1
12
作者 SHI Xiaoliang CHEN Jiajun +2 位作者 DING Hao YANG Yuanqi ZHANG Yan 《Chinese Geographical Science》 SCIE CSCD 2024年第2期342-356,共15页
Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous r... Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R2=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R2of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R2=0.680)and normal year(2017)(R2=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R2=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield. 展开更多
关键词 winter wheat yield estimation sparrow search algorithm combined with random forest(SSA-RF) machine learning multi-source indicator optimal lead time Henan Province,China
暂未订购 下载PDF
Multi-Strategy Improvement of Sparrow Search Algorithm for Cloud Manufacturing Service Composition 认领 引用
13
作者 ZHOU Liliang LI Ben +2 位作者 YU Qing DAI Guilan ZHOU Guofu 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第4期323-337,共15页
In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-... In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange.In this paper,we introduce an improved Sparrow Search Algorithm(ISSA)to address these issues.The fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target solutions.To enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation strategies.Furthermore,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively.Finally,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities.Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods.These results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition. 展开更多
关键词 cloud manufacturing service composition optimization quality of service sparrow search algorithm
暂未订购 下载PDF
Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models 认领 引用 被引量：5
14
作者 SHUI Kuan HOU Ke-peng +2 位作者 HOU Wen-wen SUN Jun-long SUN Hua-fen 《Journal of Mountain Science》 SCIE CSCD 2023年第10期2852-2868,共17页
The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration o... The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments. 展开更多
关键词 Multi-layer regression algorithm fusion Stacking gensemblelearning Sparrow search algorithm Slope safety factor Data prediction
暂未订购 下载PDF
A Modified Self-Adaptive Sparrow Search Algorithm for Robust Multi-UAV Path Planning 认领 引用 被引量：3
15
作者 SUN Zhiyuan SHEN Bo +2 位作者 PAN Anqi XUE Jiankai MA Yuhang 《Journal of Donghua University(English Edition)》 CAS 2024年第6期630-643,共14页
With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execu... With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execution,it is a nonlinear problem with constraints.Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints.At the same time,robustness should be taken into account to ensure the reliable and safe operation of the UAVs.In this paper,a self-adaptive sparrow search algorithm(SSA),denoted as DRSSA,is presented.During optimization,a dynamic population strategy is used to allocate the searching effort between exploration and exploitation;a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range;a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums.The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers(IEEE)Congress on Evolutionary Computation(CEC)2017 benchmark suite.Furthermore,a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations.Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations. 展开更多
关键词 multiple unmanned aerial vehicle(multi-UAV) path planning sparrow search algorithm(SSA) stochastic optimization
暂未订购 下载PDF
Optimization of fracture reduction robot controller based on improved sparrow algorithm 认领 引用 被引量：2
16
作者 Baichuan An Jianwen Chen +5 位作者 Hao Sun Minghuan Yin Zicheng Song Chao Zhuang Cheng Chang Minghe Liu 《Biomimetic Intelligence & Robotics》 2023年第4期16-28,共13页
The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of surgery.Improving the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is cr... The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of surgery.Improving the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is critical.To achieve this,a sparrow search algorithm(SSA)based on the Levy flight operator was proposed in this study for self-tuning the robot controller parameters.An inverse kinematic analysis of the FRR was also performed.The robot dynamics model was established using Simulink,and the inverse dynamics controller for the fracture reduction mechanism was designed using the computed torque control method.Both simulation and physical experiments were also performed.The actual motion trajectory of the actuator drive rod and its error with a desired trajectory was obtained through simulation.An optimized Levy-sparrow search algorithm(Levy-SSA)crack reduction robot controller demonstrated an overall reduction of two orders of magnitude in the reduction error,with an average error reduction of 98.74%compared with the traditional unoptimized controller.The Levy-SSA increased the convergence of the crack reduction robot control system to the optimal solution,improved the accuracy of the motion trajectory,and exhibited important implications for robot controller optimization. 展开更多
关键词 Fracture reduction robot The sparrow search algorithm Levy flight Reduction accuracy
Predicting buckling of carbon fiber composite cylindrical shells based on backpropagation neural network improved by sparrow search algorithm 认领 引用
17
作者 Wei Guan Yong-mei Zhu +1 位作者 Jun-jie Bao Jian Zhang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第12期2459-2470,共12页
The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner dia... The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites. 展开更多
关键词 Composite cylindrical shell:Carbon fiber Backpropagation neural network Sparrow search algorithm Buckling
暂未订购 下载PDF
基于ISSA-GPR的锂离子电池健康状态估计 认领 引用 被引量：7
18
作者 张梦迪 刘洋 +3 位作者 陈健 吉金鹏 姚智伟 公衍勇 《电源学报》 CSCD 北大核心 2026年第2期233-243,共11页
由于电池健康状态SOH(state-of-health)难以被直接测量,因此对SOH的准确估计对保证电池安全运行至关重要。基于此,提出了1种改进麻雀搜索算法优化高斯过程回归ISSA-GPR(improved sparrow search algorithm-Gaussian process regression... 由于电池健康状态SOH(state-of-health)难以被直接测量,因此对SOH的准确估计对保证电池安全运行至关重要。基于此,提出了1种改进麻雀搜索算法优化高斯过程回归ISSA-GPR(improved sparrow search algorithm-Gaussian process regression)锂离子电池健康状态估计方法。首先采用改进麻雀搜索算法优化高斯过程回归模型参数,构建基于改进麻雀搜索算法的高斯过程回归模型;然后分析容量增量曲线,提取表征电池容量退化的健康因子作为模型的输入,并通过改进麻雀搜索算法确定以峰值为中心峰面积的最佳电压区间长度,进而得到电池健康状态估计模型;最后利用公开的实验数据集进行验证。结果表明,所提ISSA-GPR方法能够对电池健康状态进行准确估计。 展开更多
关键词 锂离子电池 健康状态 容量增量曲线 健康因子 麻雀搜索算法 高斯过程回归
暂未订购 下载PDF
基于改进CNN-LSTM模型的在役轴承寿命预测方法 认领 引用 被引量：2
19
作者 韩允童 王靖岳 +1 位作者 侯兴达 丁建明 《机械强度》 CAS CSCD 北大核心 2026年第2期40-46,共7页
【目的】针对传统卷积神经网络(Convolutional Neural Network,CNN)-长短期记忆(Long Short-Term Memory,LSTM)网络模型参数调整复杂、预测精度受限的问题,提出一种改进的剩余寿命预测方法,旨在提升在役滚动轴承寿命预测的准确性与稳定... 【目的】针对传统卷积神经网络(Convolutional Neural Network,CNN)-长短期记忆(Long Short-Term Memory,LSTM)网络模型参数调整复杂、预测精度受限的问题,提出一种改进的剩余寿命预测方法,旨在提升在役滚动轴承寿命预测的准确性与稳定性。【方法】首先,融合黄金正弦策略来改进麻雀搜索算法(Golden Sparrow Search Algorithm,GSSA),以增强其全局与局部搜索能力,实现对CNN-LSTM关键参数的自适应优化;其次,构建基于相关性、单调性和鲁棒性的特征筛选体系,筛选出高敏感性退化特征;最后,利用PHM2012轴承数据集,建立GSSA-CNN-LSTM预测模型,通过对比反向传播(Back Propagation,BP)神经网络与CNN-LSTM模型验证其有效性。【结果】结果表明,所提GSSACNN-LSTM模型在均方根误差、平均绝对误差与均方误差上,较BP神经网络与CNN-LSTM模型分别降低了67.61%、83.71%、80.89%与61.18%、78.78%、51.02%,确定系数更接近1,显著提升了预测精度与鲁棒性。 展开更多
关键词 滚动轴承 黄金正弦策略 麻雀搜索算法 剩余寿命预测 优化
暂未订购 下载PDF
融合SBERT与自适应HDBSCAN算法的技术主题识别及演化研究——以人工智能领域为例 认领 引用 被引量：2
20
作者 孙文晶 马捷 郝志远 《情报理论与实践》 CSSCI 北大核心 2026年第3期139-149,共11页
[目的/意义]文章旨在构建一种自适应技术主题识别模型,以此来对领域技术主题展开精准识别与演化分析。[方法/过程]以人工智能领域为例,通过选取该领域2014—2024年期间的相关论文和专利数据为实验对象开展实证研究。首先,利用Sentence-B... [目的/意义]文章旨在构建一种自适应技术主题识别模型,以此来对领域技术主题展开精准识别与演化分析。[方法/过程]以人工智能领域为例,通过选取该领域2014—2024年期间的相关论文和专利数据为实验对象开展实证研究。首先,利用Sentence-BERT(SBERT)模型实现文本向量化表示,并选取HDBSCAN作为技术主题识别的基础模型;其次,引入群体智能优化的技术理念,并提出创新的增强型麻雀搜索算法(Advanced Sparrow Search Algorithm,ASSA)来实现HDBSCAN模型超参数的自适应选取过程,进而形成ASSA-HDBSCAN自适应技术主题识别模型;最后,基于所识别技术主题之间的余弦相似度与主题重要度指标揭示出人工智能领域关键技术的演化趋势与发展情况。[结果/结论]与基线模型相比,所提模型在轮廓系数与主题一致性指数两个评价指标上均呈现出了明显优势。此外,所得主题词演化结果能够细粒度呈现领域技术的发展趋势,这进一步印证了该模型的有效性与可行性。[创新/价值]本文所提改进优化算法ASSA可为主题识别模型由“被动适应数据”向“主动适应数据”进阶转化提供重要技术支撑。 展开更多
关键词 技术主题识别 主题演化 麻雀搜索算法 自适应HDBSCAN 群体智能优化
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 98 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈