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17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Zhu, Jiaqi Ning, Weibo Yuan, Ye Chen, Hongjiang Zhou, Weijun Tan, Yecheng He, Shuxing Hu, Jun Fan, Zhun Shantou Univ Shantou Peoples R China Shantou Univ Med Coll Affiliated Hosp 1 Shantou Peoples R China

ISBN: (纸本)9781665476874

Hand-eye calibration methods for surgical robots are employed to derive a transformation between the robot's base motor and visual coordinate systems. Accurately completing hand-eye calibration procedures provides an important guarantee that a surgical robot will exhibit positioning and execution accuracy sufficient for assisting surgeons in successfully completing surgical procedures. To improve the accuracy of robot hand-eye calibration methods based on backpropagation neural network (BPNN) models, we propose a modified BP neural network optimized using the sparrow search algorithm for hand-eye calibration model (TSSABPNN), which can enhance population initialization by applying tent mapping. Furthermore, we also design a new sliding 3D calibration tool. The sparrow search algorithm exhibits good local exploration ability, and we introduce a tent map with ergodic characteristics to initialize the sparrow population information, which further improves the network's global search ability and convergence rate. Finally, we experimentally analyze four calibration models: TSSABP NN model, a BP NN model optimized using a genetic algorithm of simulated annealing (GASABPNN), an unoptimized BP NN model, and the traditional singular value decomposition method. The results indicate that the proposed TSSABP NN model exhibits the maximum calibration precision and best robustness and iteratively converges faster.

关键词： Hand-eye calibration Nonlinear coordinate convert Intelligent optimization sparrow search algorithm Neural Network

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Short-Term Wind Speed Prediction Study Based on Variational Mode Decompositions-sparrow search algorithm-Gated Recurrent Units

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PROCESSES 2024年第8期12卷 1741页

作者： Yang, Tongrui Guo, Xihao Qian, Guowei Sun Yat Sen Univ Sch Math Guangzhou 510275 Peoples R China Sun Yat Sen Univ Sch Ocean Engn & Technol Zhuhai 519082 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519082 Peoples R China

Improving the accuracy of short-term wind speed predictions is crucial for mitigating the impact on power systems when integrating wind power into an electricity grid. This study developed a hybrid short-term wind speed prediction method, termed VMD-SSA-GRU, by combining variational mode decomposition (VMD) with gated recurrent units (GRUs) and optimizing it using a sparrow search algorithm (SSA). Initially, VMD was used to decompose the wind speed time series into subtime series. After reconstructing these subtime series, a GRU model was employed to establish separate prediction models for each series. Furthermore, an enhanced SSA was proposed to optimize the hyperparameters of the GRU model, which improved the prediction accuracy. Ultimately, the sub-series predictions were aggregated to produce the final wind speed prediction values. The predictive accuracy of this model was validated using the wind speed data measured at a meteorological station near a bridge site. The performance of the VMD-SSA-GRU model was compared with several other hybrid models, including those using wavelet transform, long short-term memory, and other neural networks. Comparably, the RMSE value of the VMD-SSA-GRU model was lower by 25.3%, 60.2%, and 61.7% in comparison to the VMD-SSA-LSTM, VMD-GRU, and VMD-LSTM models, respectively. The experimental results demonstrated that the proposed method achieved higher prediction accuracy than traditional methods.

关键词： short-term wind speed wind speed prediction sparrow search algorithm variational mode decomposition gated recurrent units

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A novel adaptive neighborhood rough sets based on sparrow search algorithm and feature selection

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INFORMATION SCIENCES 2024年 679卷

作者： Liu, Caihui Lin, Bowen Miao, Duoqian Gannan Normal Univ Dept Math & Comp Sci Ganzhou 341000 Jiangxi Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China

Neighborhood rough sets-based methods have been widely used for feature selection. However, the existing methods have some problems in neighborhood construction, such as the application of the same neighborhood radius for all samples. Thus, this paper proposed a novel adaptive neighborhood rough set model based on sparrow search algorithm (SSA) to tackle the above problems, and applied the model to feature selection. First, we reconsidered the problems mentioned above from the viewpoint of optimization where the neighborhood radius of the target sample is considered as the solution to the optimization, and the maximum percentage of the label of the neighborhood formed is considered as the target to the optimization. Second, SSA is introduced to design the adaptive neighborhood construction to tackle the optimization problem where all candidate neighborhood radii of the target sample are considered as sparrows, the maximum and minimum distances between the target sample and other samples are considered as the search range, and the maximum label rate defined in this paper is considered as the search target. Then, a novel adaptive neighborhood rough set model is proposed by using the adaptive neighborhood construction. Third, we proposed a feature selection algorithm based on the adaptive neighborhood rough set model. Finally, the experimental results on seventeen datasets demonstrate the effectiveness of our algorithm. The running time of the proposed algorithm is at least one time less than classical algorithms under the condition that the classification performance is better, the accuracy increases 3% and the balanced accuracy increases 4%.

关键词： Adaptive neighborhood rough set model Feature selection sparrow search algorithm

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Iris recognition method based on improved sparrow search algorithm and BPNN

ENGINEERING RESEARCH EXPRESS

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ENGINEERING REsearch EXPRESS 2025年第1期7卷 015259-015259页

作者： Su, Ge Tian, Ye Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun 130022 Peoples R China Changchun Univ Sci & Technol Zhongshan Inst Zhongshan 528437 Peoples R China

Iris recognition is a very popular and efficient biometric method that has a wide range of applications in finance, security and many other areas. The uniqueness and invariance of the iris makes it highly accurate in identification. Although there are many approaches to iris recognition, such as deep learning based methods, drawbacks such as high computational complexity, high memory requirements, and long training times still exist. Since BP neural networks have the advantages of simple structure and short training time, this paper, an iris recognition method based on improved sparrow search algorithm (CRSSA) optimized back propagation neural network (BPNN) is proposed. The sparrow search algorithm is improved using improved Kent chaos mapping strategy, Dynamic adaptive weight strategy and Logarithmic spiral-based random walk strategy. Then the improved algorithm is used to optimize the BP neural network. Finally, the improved network is used as a classifier for iris recognition. Preprocessing of iris images using Hough transform based processing. The iris image is processed using feature extraction methods based on first order statistical measures and second order statistical measures (F-S) and finally iris recognition is performed using the method proposed in this paper. The experimental results show that the method proposed in this paper exhibits higher recognition accuracy and stability than other algorithms in the experiment on CSAIA-iris-V4 and JLU-4.0 datasets. The improved sparrow search algorithm (CRSSA) proposed in this paper also performs better than the other algorithms in the experiments in terms of convergence accuracy and stability.

关键词： sparrow search algorithm iris recognition random walk swarm intelligence algorithm

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Improved sparrow search algorithm Based on Multistrategy Collaborative Optimization Performance and Path Planning Applications

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PROCESSES 2024年第12期12卷 2775页

作者： Xu, Kunpeng Chen, Yue Zhang, Xuanshuo Ge, Yizheng Zhang, Xu Li, Longhai Guo, Ce Xuzhou Coll Ind Technol Coll Mech & Elect Engn Xuzhou 221140 Peoples R China Xuzhou Univ Technol Sch Mech & Elect Engn Xuzhou 221018 Peoples R China Nanjing Univ Aeronaut & Astronaut Inst Bioinspired Struct & Surface Engn 29 Yudao St Nanjing 210016 Peoples R China

To address the problems of limited population diversity and a tendency to converge prematurely to local optima in the original sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) based on multi-strategy collaborative optimization is proposed. ISSA employs three strategies to enhance performance: introducing one-dimensional composite chaotic mapping SPM to generate the initial sparrow population, thus enriching population diversity;introducing the dung beetle dancing search behavior strategy to strengthen the algorithm's ability to jump out of local optima;integrating the adaptive t-variation improvement strategy to balance global exploration and local exploitation capabilities. Through experiments with 23 benchmark test functions and comparison with algorithms such as PSO, GWO, WOA, and SSA, the advantages of ISSA in convergence speed and optimization accuracy are verified. In the application of robot path planning, compared with SSA, ISSA exhibits shorter path lengths, fewer turnings, and higher planning efficiency in both single-target point and multi-target point path planning. Especially in multi-target point path planning, as the obstacle rate increases, ISSA can more effectively find the shortest path. Its traversal order is different from that of SSA, making the planned path smoother and with fewer intersections. The results show that ISSA has significant superiority in both algorithm performance and path planning applications.

关键词： sparrow search algorithm one-dimensional composite chaotic mapping dung beetle dancing search behavior strategy adaptive t-distribution robotic path planning

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A Wind Farm Power Maximization Method Based on Multi-Strategy Improved sparrow search algorithm

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2024年第3期146卷 031012-031012页

作者： Bo, Gu Man, Dandan Meng, Zhong Zhang, Hongtao Hu, Hao North China Univ Water Resources & Elect Power Sch Elect Engn Zhengzhou 450011 Peoples R China Yellow River Conservancy Tech Inst Kaifeng 475000 Peoples R China

For large-scale constructed wind farms, reducing wake loss and improving the overall output power are the main objectives for their optimal operation. Therefore, a wind farm power maximization method based on a multi-strategy improved sparrow search algorithm (MS-ISSA) is proposed in this paper. Integrating the wake propagation mechanism of wind turbines and the characteristics of the classic Jensen wake model, the Jensen-Gaussian wake model and wake superposition model were constructed to accurately calculate the wind farm wake distribution. The constructed Jensen-Gaussian wake model and wake superposition model can accurately describe the non-uniform distribution characteristics of wake velocity. The Sin chaotic model, Cauchy distribution, and hyperparameter adaptive adjustment strategy were used to improve the sparrow search algorithm (SSA), and the optimization ability, convergence speed, and stability of the SSA were improved. Accordingly, considering the maximum output power of the wind farm as the optimization target and axial induction factor as the optimization variable, a coordinated optimization model for wind turbines based on MS-ISSA was proposed to realize the coordinated optimal operation of wind turbines with reduced wake loss. Considering the Danish Horns Rev wind farm as the research object, the results of optimization using particle swarm optimization algorithm, whale optimization algorithm, basic sparrow search algorithm, and MS-ISSA were calculated and analyzed. The calculation results revealed that under different incoming wind conditions, the MS-ISSA exhibited better optimization results than the other optimization algorithms.

关键词： wind farm wind turbine sparrow search algorithm wake loss coordinated optimization clean energy renewable energy

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Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

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ENERGY 2024年 308卷

作者： Hou, Guolian Ye, Lingling Huang, Ting Huang, Congzhi North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China North China Elect Power Univ Key Lab Power Stn Energy Transfer Convers & Syst Beijing 102206 Peoples R China

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multireceptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis.

关键词： Intelligent modeling Combined heat and power unit Full operating conditions Crossformer Bidirectional gated recurrent unit sparrow search algorithm

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The Method and Application of a Variational Modal Decomposition Based on sparrow search algorithm 7

The Method and Application of a Variational Modal Decomposit...

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7th International Conference on Robotics and Automation Engineering (ICRAE)

作者： Yang, Xuefeng Zhou, Xiaojun Fu, Yimeng Wan, Bowen Xiao, Peng Zhejiang Univ Sch Mech Engn Hangzhou Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Bymoon Sci & Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9781665489188

Heavy machinery is often accompanied by vibration during operation, which is reciprocating, unstable, shocking and has a significant impact on the life of its parts. Traditional time-frequency analysis methods are difficult to effectively decompose the signal and extract relevant information. In this paper, a variational modal decomposition based on the sparrow search algorithm is proposed. In this method, a variational modal decomposition algorithm is introduced and improved, and the fitness function is constructed from the perspective of envelope entropy and signal energy, and then the minimum fitness value is solved by the sparrow search algorithm to optimize the parameters. A comparative study is made with variational modal decomposition algorithm and the algorithm in this paper, the numerical simulation signal decomposition results show that both the algorithm in this paper and the variational modal decomposition algorithm can effectively identify and decompose the signal, but the algorithm in this paper can better control the signal leakage without causing modal aliasing;the hammer test further shows that the algorithm in this paper can effectively deal with the shock signal;finally relying on the Efficient Braking Performance Test Bed for braking tests, the test shows that the algorithm in this paper can effectively extract signal features and improve the accuracy of signal decomposition, which has certain practical application value.

关键词： heavy machinery variational mode decomposition sparrow search algorithm hammer test efficient braking test component

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Multi-objective Antenna Design Based on Improved sparrow search algorithm to Optimize BP Neural Network Surrogate Model 2

Multi-objective Antenna Design Based on Improved Sparrow Sea...

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2nd IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) / 7th International Workshop on Pattern Recognition (IWPR)

作者： Wang, Zhongxin Qin, Jian Hu, Zijiang He, Jian Guangzhou Univ Sch Elect & Commun Engn Guangzhou Peoples R China

ISBN: (数字)9781665482233

ISBN: (纸本)9781665482233

In response to the problems of time-consuming and low potency of traditional antenna design methods, a multi objective antenna design method based on MISSA-BP surrogate model is proposed. Meanwhile, for the problems of sparrow search algorithm, such as single population and difficulty in reaching global optimum, proposes a multi-strategy improved sparrow search algorithm (MISSA), three strategies of reverse learning, inertial weight and adaptive t-distribution are introduced for improvement to enrich the population diversity and strengthen the flexibility of the algorithm to reach the optimum result. The experimental results show that the improved sparrow search algorithm has higher improvement performance, and the proposed MISSA-BP surrogate model can be bound to the multiobjective optimization algorithm to achieve fast multi-objective antenna design.

关键词： sparrow search algorithm surrogate model reverse learning t-distribution antenna design

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An improved sparrow search algorithm for mobile robot path planning 41

An improved sparrow search algorithm for mobile robot path p...

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第41届中国控制会议

作者： Dongmei Wu Fengming Hao Chengzhi Yuan Yangzheng Li College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Jiangsu Engineering Lab for IoT Intelligent Robots Nanjing University of Posts and Telecommunications

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper proposes an improved sparrow search algorithm for robot path *** improved heuristic algorithm introduces Tent map and opposition-based learning scheme into sparrow search *** Tent map is applied to initialize population for greater diversity,and opposition-based learning enhances the diversity of the population in evolution process,so as to avoid the premature convergence of the *** proposed algorithm is adopted to mobile robot path *** results show that the improved algorithm can guide the mobile robot to find the optimal path quickly,and also prove the superiority and effectiveness of the proposed algorithm over the original algorithm.

关键词： sparrow search algorithm robot path planning opposition-based learning optimization

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