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Structural Damage Diagnosis Based onMulti-Stage Sparrow Search Algorithm 认领 引用
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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
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Residual Strength Prediction of Corroded Pipelines Based on Sparrow Search Algorithm-Optimized Kernel Extreme Learning Machine 认领 引用
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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
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NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization 认领 引用
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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
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An NOMA-VLC power allocation scheme for multi-user based on sparrow search algorithm 认领 引用 被引量：1
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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
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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
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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
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Improved sparrow search algorithm for inversion of geometric parameters of earthquake source faults 认领 引用
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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
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Optimized control of grid-connected photovoltaic systems:Robust PI controller based on sparrow search algorithm for smart microgrid application 认领 引用
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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
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A Clustering Model Based on Density Peak Clustering and the Sparrow Search Algorithm for VANETs 认领 引用
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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
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A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems 认领 引用 被引量：15
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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
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基于ISSA-GPR的锂离子电池健康状态估计 认领 引用 被引量：7
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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
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Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm 认领 引用 被引量：5
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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
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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
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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
基于BWO-VMD-ISSA-LSTM的交通运输业碳排放预测研究 认领 引用 被引量：1
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作者 王庆荣 王俊杰 +3 位作者 朱昌锋 张金鹏 何润田 刘心康 《计算机工程与应用》 EI CSCD 北大核心 2026年第10期376-388,共13页
针对交通运输业碳排放量的非线性影响预测精度的问题,提出了一种结合白鲸优化算法(beluga whale optimization,BWO)、变分模态分解(variational mode decomposition,VMD)、改进麻雀搜索算法(improved sparrow search algorithm,ISSA)及L... 针对交通运输业碳排放量的非线性影响预测精度的问题,提出了一种结合白鲸优化算法(beluga whale optimization,BWO)、变分模态分解(variational mode decomposition,VMD)、改进麻雀搜索算法(improved sparrow search algorithm,ISSA)及LSTM的碳排放预测模型。引入最大互信息系数(maximum information coefficient,MIC)提取影响碳排放量的主要因素,剔除冗余特征。利用BWO对VMD的分解模态数和惩罚因子寻优,增强两参数间的协调性,进而将碳排放量分解为不同频率的模态分量和剩余分量,削弱原始碳排放量的非线性;通过在LSTM的输入端嵌入特征注意力机制(feature attention mechanism,FA),突出关键输入特征。引入基于改进Tent混沌映射、动态步长权重因子、混合变异算子和精英反向学习的混合策略改进SSA算法,避免算法陷入局部最优。对各模态分量分别构建基于ISSA-LSTM的预测模型,并对预测结果进行集成。采用中国交通运输业1990—2019年的碳排放数据对模型进行验证,结果表明,所提模型较最优对比模型的RMSE、MAE和MAPE分别降低了26.28%、31.64%和33.32%,能够有效地预测交通运输业碳排放量。 展开更多
关键词 交通运输业 碳排放预测 白鲸优化算法 变分模态分解 麻雀搜索算法 最大互信息系数
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基于MWMOTE和SSA-KELM的电力系统静态电压稳定评估 认领 引用 被引量：1
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作者 刘颂凯 曹俊 +4 位作者 苏攀 高坤 吴宇恒 万明 艾迪 《电力科学与技术学报》 CAS CSCD 北大核心 2026年第1期13-22,共10页
基于数据驱动的电力系统静态电压稳定评估方法通常存在初始数据样本类别不平衡问题,导致数据驱动评估模型的性能受到很大的影响。为此,提出一种基于带多数类权重的少数类过采样技术(majority weighted minority oversampling technique,... 基于数据驱动的电力系统静态电压稳定评估方法通常存在初始数据样本类别不平衡问题,导致数据驱动评估模型的性能受到很大的影响。为此,提出一种基于带多数类权重的少数类过采样技术(majority weighted minority oversampling technique,MWMOTE)和麻雀搜索算法优化核极限学习机(sparrow search algorithm-kernel extreme learning machine,SSA-KELM)的电力系统静态电压稳定评估方法。首先,利用MWMOTE解决样本类别不平衡问题,增加样本多样性;然后,使用SSA优化KELM模型参数,构建基于SSA-KELM的电力系统静态电压稳定评估模型;最后,在新英格兰10机39节点系统上进行验证。测试结果表明,所提方法不仅能够有效应对样本类别不平衡问题,还具有良好的评估准确率和泛化能力。 展开更多
关键词 样本类别不平衡 静态电压稳定评估 带多数类权重的少数类过采样技术 麻雀搜索算法 核极限学习机
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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
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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
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基于SSA-VMD-GRU组合模型的桥梁监测缺失数据重构方法研究 认领 引用
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作者 周宇 周明扬 +2 位作者 狄生奎 郭家骥 黄继源 《振动与冲击》 EI CSCD 北大核心 2026年第3期115-123,共9页
针对桥梁健康监测数据因环境干扰或传感器故障导致的异常或缺失,提出了一种基于麻雀搜索算法(sparrow search algorithm,SSA)共同优化变分模态分解(variational mode decomposition,VMD)和门控循环单元(gated recurrent units,GRU)的桥... 针对桥梁健康监测数据因环境干扰或传感器故障导致的异常或缺失,提出了一种基于麻雀搜索算法(sparrow search algorithm,SSA)共同优化变分模态分解(variational mode decomposition,VMD)和门控循环单元(gated recurrent units,GRU)的桥梁异常监测数据修复方法。研究利用SSA对VMD中分解层数K和惩罚因子α进行寻优以获取准确结构响应,选择SSA对GRU关键超参数进行优化,通过训练使模型达到最佳状态后,将分解后的信号作为输入进行预测修复,以重构桥梁缺失监测数据,通过对比单一GRU模型、VMD-GRU模型预测结果,以均方根误差、平均绝对误差、平均绝对百分比误差和R2作为误差指标来评价所提方法的科学性与实用性。研究表明,所提方法可在非经验指导下获得最佳参数组合,挠度测试集均方根误差为6.070 2%,应变测试集均方根误差仅为0.150 0%,该方法适用于桥梁异常或缺失监测数据的重构,能够提高数据质量和数据使用的正确率,为桥梁健康监测与决策提供方法基础。 展开更多
关键词 桥梁健康监测 异常监测数据 麻雀搜索算法(SSA) 变分模态分解(VMD) 门控循环单元(GRU) 数据重构
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考虑平均应力的非高斯疲劳损伤预测:一种基于SSA优化XG-Boost的预测算法 认领 引用
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作者 李锦华 曾锦航 +3 位作者 李芳华 崔胜超 邹秀龙 李春祥 《振动与冲击》 EI CSCD 北大核心 2026年第13期10-18,共9页
疲劳损伤分析对于承受各类随机荷载的工程结构是必不可少的,非高斯随机荷载以及动静荷载叠加导致的平均应力,都使疲劳损伤预测变得更为复杂。雨流计数法在非高斯随机过程中考虑每个应力循环的平均应力时需要大量的计算时间和成本;而频... 疲劳损伤分析对于承受各类随机荷载的工程结构是必不可少的,非高斯随机荷载以及动静荷载叠加导致的平均应力,都使疲劳损伤预测变得更为复杂。雨流计数法在非高斯随机过程中考虑每个应力循环的平均应力时需要大量的计算时间和成本;而频域法虽然能快速估计疲劳损伤率,但难以有效考虑每个雨流均值的影响。因此,基于Dirlik提出的频域方法以及Niesłony和Böhm提出的功率谱密度修正法,建立了极端梯度提升(extreme gradient boosting,XG-Boost)模型预测考虑平均应力效应的宽带非高斯疲劳损伤,并使用麻雀搜索算法(sparrow search algorithm,SSA)寻优。基于不同的功率谱,对S-N曲线的k值、平均应力、极限抗拉强度以及偏度和峰度进行了大量的数值模拟。得到的数据库用于增强XG-Boost模型的泛化性,采用雨流计数法计算的疲劳损伤率为精准参照。最终结果表明,所开发的XG-Boost模型可以精准预测非高斯疲劳损伤。 展开更多
关键词 疲劳损伤分析 平均应力 宽带非高斯过程 麻雀搜索算法(SSA) 极端梯度提升(XG-Boost)模型 频域法
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基于SCSSA-CNN-BiLSTM神经网络的厌氧发酵产气预测 认领 引用
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作者 甄箫斐 焦若楠 +1 位作者 董樾洋 詹寒 《环境工程技术学报》 CAS CSCD 北大核心 2026年第1期279-289,共11页
厌氧发酵作为一种高效的有机废物处理技术,能够将农业废物转化为沼气,实现资源的循环利用和能源的可持续供应。厌氧发酵过程受到反应底物碳氮比、pH、挥发性脂肪酸、氨氮浓度以及化学需氧量等因素的影响。为探究厌氧发酵的规律,进行混... 厌氧发酵作为一种高效的有机废物处理技术,能够将农业废物转化为沼气,实现资源的循环利用和能源的可持续供应。厌氧发酵过程受到反应底物碳氮比、pH、挥发性脂肪酸、氨氮浓度以及化学需氧量等因素的影响。为探究厌氧发酵的规律,进行混合原料厌氧发酵产气实验,反应底物中牛粪与玉米秸秆的配比分别为1:1、2:1、3:1,设置3组平行实验,以确保实验结果的可靠性和可重复性。创建了正余弦与柯西变异策略优化的麻雀搜索算法(SCSSA),并将其对卷积双向记忆神经网络(CNNBiLSTM)的超参数进行优化,选择反应时间、牛粪与玉米秸秆配比、pH、挥发性脂肪酸、氨氮浓度以及化学需氧量作为模型的输入参数,日产气量和日甲烷产量作为输出参数。结果表明,牛粪与玉米秸秆配比为3:1时,甲烷产量最多,配比1:1实验组次之,配比2:1实验组最小。基于SCSSA-CNN-BiLSTM混合原料厌氧发酵产气预测模型的日产气量准确率达95.29%,日甲烷产量准确率达95.87%,拟合优度(R2)达到了0.972。本研究解决了传统麻雀搜索算法模型易过早收敛导致陷入局部最优的问题,并提高了全局搜索能力,为实际实验提供了依据。 展开更多
关键词 牛粪 玉米秸秆 厌氧发酵 神经网络 麻雀搜索算法 产气预测
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基于ISSA-CNN-BiLSTM的电力碳排放强度动态预测方法 认领 引用
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作者 李宏伟 王来君 +3 位作者 李帅兵 贠韫韵 杨立霞 康永强 《中国电力》 CSCD 北大核心 2026年第7期66-77,共12页
电力碳排放强度是衡量电力系统碳排放水平的重要指标,精确计算并预测电力碳排放强度对于有效实施碳排放管理策略、科学制定减排方案具有重要意义。为此,提出一种动态碳排放强度计算及预测方法。基于燃煤机组运行煤耗与负荷的函数关系,... 电力碳排放强度是衡量电力系统碳排放水平的重要指标,精确计算并预测电力碳排放强度对于有效实施碳排放管理策略、科学制定减排方案具有重要意义。为此,提出一种动态碳排放强度计算及预测方法。基于燃煤机组运行煤耗与负荷的函数关系,构建燃煤机组动态碳排放强度模型。融合发电机组出力特性和动态碳排放强度,形成电网级碳排放强度实时计算模型。通过卷积神经网络(convolutional neural network,CNN)提取动态碳排放强度数据特征,输入到双向长短期记忆网络(bidirectional long short-term memory,BiLSTM),引入改进麻雀优化算法(improved sparrow search algorithm,ISSA),集成Circle初始化种群、自适应因子、柯西变异和Sine映射扰动策略,优化BiLSTM超参数配置,最终构建ISSA-CNN-BiLSTM混合模型,实现短期碳排放强度高精度预测。仿真计算与测试结果表明,与长短期记忆网络(long short-term memory,LSTM)等模型相比,提出的ISSA-CNN-BiLSTM模型在碳排放强度预测中展现出了更高的预测精度和更强的泛化能力。 展开更多
关键词 碳排放强度 长短期记忆网络 麻雀搜索算法
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基于SCSSA-BiLSTM的变压器故障诊断模型 认领 引用 被引量：1
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作者 汪繁荣 李州 《南方电网技术》 CSCD 北大核心 2026年第2期78-86,共9页
针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)... 针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。 展开更多
关键词 变压器 故障诊断 麻雀搜索算法 双向长短期记忆网络 诊断精度
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