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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：1
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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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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Improved Cuckoo Search Algorithm for Engineering Optimization Problems 认领 引用
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作者 Shao-Qiang Ye Azlan Mohd Zain Yusliza Yusoff 《Computers, Materials & Continua》 SCIE EI 2026年第4期1607-1631,共25页
Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting incr... Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms. 展开更多
关键词 Cuckoo search algorithm chaotic transformation population division adaptive update strategy Cauchy distribution
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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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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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Optimizing Cancer Classification and Gene Discovery with an Adaptive Learning Search Algorithm for Microarray Analysis 认领 引用
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作者 Chiwen Qu Heng Yao +1 位作者 Tingjiang Pan Zenghui Lu 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第2期901-930,共30页
DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we presen... DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we present Fs-LSA (F-score based Learning Search Algorithm), a novel gene selection algorithm designed to enhance the precision and efficiency of target gene identification from microarray data for cancer classification. This algorithm is divided into two phases: the first leverages F-score values to prioritize and select feature genes with the most significant differential expression;the second phase introduces our Learning Search Algorithm (LSA), which harnesses swarm intelligence to identify the optimal subset among the remaining genes. Inspired by human social learning, LSA integrates historical data and collective intelligence for a thorough search, with a dynamic control mechanism that balances exploration and refinement, thereby enhancing the gene selection process. We conducted a rigorous validation of Fs-LSA’s performance using eight publicly available cancer microarray expression datasets. Fs-LSA achieved accuracy, precision, sensitivity, and F1-score values of 0.9932, 0.9923, 0.9962, and 0.994, respectively. Comparative analyses with state-of-the-art algorithms revealed Fs-LSA’s superior performance in terms of simplicity and efficiency. Additionally, we validated the algorithm’s efficacy independently using glioblastoma data from GEO and TCGA databases. It was significantly superior to those of the comparison algorithms. Importantly, the driver genes identified by Fs-LSA were instrumental in developing a predictive model as an independent prognostic indicator for glioblastoma, underscoring Fs-LSA’s transformative potential in genomics and personalized medicine. 展开更多
关键词 Gene selection Learning search algorithm Gene expression data Classification
Optimal load frequency control system for two-area connected via AC/DC link using cuckoo search algorithm 认领 引用
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作者 Gaber EL-SAADY Alexey MIKHAYLOV +2 位作者 Nora BARANYAI Mahrous AHMED Mahmoud HEMEIDA 《虚拟现实与智能硬件（中英文）》 EI CSCD 2025年第3期299-316,共18页
Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a ... Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances. 展开更多
关键词 Load frequency control Cuckoo search algorithm PI controllers State space modeling
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Adaptive Multi-Learning Cooperation Search Algorithm for Photovoltaic Model Parameter Identification 认领 引用
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作者 Xu Chen Shuai Wang Kaixun He 《Computers, Materials & Continua》 SCIE EI 2025年第10期1779-1806,共28页
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in... Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms. 展开更多
关键词 Photovoltaic model parameter identification cooperation search algorithm adaptive multiple learning chaotic grouping reflection
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Deep Learning Mixed Hyper-Parameter Optimization Based on Improved Cuckoo Search Algorithm 认领 引用
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作者 TONG Yu CHEN Rong HU Biling 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第2期195-204,共10页
Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,... Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method. 展开更多
关键词 improved Cuckoo Search algorithm mixed hyper-parameter optimization deep learning
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An Advanced Bald Eagle Search Algorithm for Image Enhancement 认领 引用
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作者 Pei Hu Yibo Han Jeng-Shyang Pan 《Computers, Materials & Continua》 SCIE EI 2025年第3期4485-4501,共17页
Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algori... Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images. 展开更多
关键词 Image enhancement gamma correction retinex bald eagle search algorithm
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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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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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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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Energy Optimization Strategy for Reconfigurable Distribution Network with High Renewable Penetration Based on Bald Eagle Search Algorithm 认领 引用
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作者 Jian Wang Hui Qi +2 位作者 Lingyi Ji Zhengya Tang Hui Qian 《Energy Engineering》 EI 2025年第11期4635-4651,共17页
This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,mainte... This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,maintenance and operating expenses of energy storage systems,diesel generator operational costs,typical daily load profiles,and power balance constraints.A penalty term for power backflow is incorporated into the objective function to discourage undesirable reverse flows.The Bald Eagle Search(BES)meta-heuristic is adopted to solve the resulting constrained optimization problem.Numerical simulations under multiple load scenarios demonstrate that the proposed method effectively reduces operating cost while preventing power backflow and maintaining secure operation of the distribution network. 展开更多
关键词 Reconfigurable distribution networks energy optimization management bald eagle search algorithm
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Optimized quantum random-walk search algorithm for multi-solution search 认领 引用 被引量：4
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作者 张宇超 鲍皖苏 +1 位作者 汪翔 付向群 《Chinese Physics B》 SCIE EI CAS CSCD 2015年第11期133-139,共7页
This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the se... This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value. 展开更多
关键词 quantum search algorithm quantum random walk multi-solution abstract search algorithm
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An Optimization Method for Reducing Losses in Distribution Networks Based on Tabu Search Algorithm 认领 引用
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作者 Jiaqian Zhao Xiufang Gu +1 位作者 Xiaoyu Wei Mingyu Bao 《Journal of Electronic Research and Application》 2025年第2期181-190,共10页
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio... With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning. 展开更多
关键词 Distribution network Loss reduction measures Economy Optimization model Tabu search algorithm
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Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm 认领 引用 被引量：10
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作者 Qi-Hong Feng Shan-Shan Li +2 位作者 Xian-Min Zhang Xiao-Fei Gao Ji-Hui Ni 《Petroleum Science》 SCIE CAS CSCD 2022年第6期2879-2894,共16页
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T... Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development. 展开更多
关键词 Well production Optimization efficiency Streamline simulation Streamline feature Objective function Bayesian adaptive direct search algorithm
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A Multi-Layered Gravitational Search Algorithm for Function Optimization and Real-World Problems 认领 引用 被引量：14
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作者 Yirui Wang Shangce Gao +1 位作者 Mengchu Zhou Yang Yu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第1期94-109,共16页
A gravitational search algorithm(GSA)uses gravitational force among individuals to evolve population.Though GSA is an effective population-based algorithm,it exhibits low search performance and premature convergence.T... A gravitational search algorithm(GSA)uses gravitational force among individuals to evolve population.Though GSA is an effective population-based algorithm,it exhibits low search performance and premature convergence.To ameliorate these issues,this work proposes a multi-layered GSA called MLGSA.Inspired by the two-layered structure of GSA,four layers consisting of population,iteration-best,personal-best and global-best layers are constructed.Hierarchical interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population.Performance comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low,medium and high dimensions demonstrates that MLGSA is the most competitive one.It is also compared with four particle swarm optimization variants to verify its excellent performance.Moreover,the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its performance.The relationship between its population diversity and fitness diversity is analyzed to clarify its search performance.Its computational complexity is given to show its efficiency.Finally,it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality. 展开更多
关键词 Artificial intelligence exploration and exploitation gravitational search algorithm hierarchical interaction hierarchy machine learning population structure
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