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sparrow search algorithm optimized LSTM model for CSI-based indoor localization

EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-16页

作者： Wang, Yan Zhou, Yuqing Northeastern Univ Dept Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Hebei Peoples R China

In indoor positioning, Channel State Information (CSI) holds significant importance. CSI provides detailed channel characteristics, including amplitude and phase information, enabling the positioning system to estimate device locations more accurately. Compared to traditional RSSI methods, CSI offers higher positioning accuracy and better interference resistance. Additionally, it supports various advanced positioning algorithms, making it suitable for complex indoor environments and enabling real-time precise positioning. In this study, a localization method based on CSI phase and amplitude data is proposed with the aim of improving the accuracy and robustness of indoor localization. In the offline training phase, for each reference point, we construct a new dataset using pre-processed amplitude and phase data, which are transformed into image data for pre-training to extract features. Subsequently, a CNN-LSTM neural network with an integrated attention mechanism is trained to capture complex spatio-temporal relationships in the data. However, manual tuning of neural network hyperparameters has its limitations. Therefore, this paper introduces the sparrow search algorithm (SSA)bio-inspired algorithm to optimize the combination of hyperparameters, aiming to obtain a more optimal model performance. During the online orientation phase, CSI data collected from the target device is processed using the same steps as in the offline phase to convert it into images. The trained model is then used to perform regression predictions on test points, enabling localization of the target position. To assess the effectiveness of our proposed approach, we ran tests in both open and complicated laboratory settings. Comparisons were made with several existing indoor localization solutions (such as PCNB, MIMO, LSTM, and FIFS), demonstrating that our approach exhibits significant advantages regarding localization accuracy and robustness.

关键词： Indoor localization Channel state information Long short-term memory sparrow search algorithm Attention mechanism

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Time-optimal trajectory planning for 6R manipulator arm based on chaotic improved sparrow search algorithm

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS REsearch AND APPLICATION 2025年

作者： Jiao, Yinjia Zhao, Yujie Wen, Shiguang Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China

Purpose Trajectory planning is a core aspect of manipulator operation, directly influencing its performance. This paper aims to introduce a chaotic improved sparrow search algorithm (CISSA) to optimize hybrid polynomial-interpolated trajectories, enhancing the efficiency and precision of manipulator trajectory planning. Design/methodology/approach The proposed approach leverages 3-5-3 polynomial interpolation to construct the motion trajectory of a 6R manipulator. To optimize the trajectory over time, the sparrow search algorithm is enhanced with chaotic mapping, a discoverer dispersion strategy, positional limiting mechanisms and Brownian motion. These enhancements collectively reduce the manipulator's runtime while meeting operational requirements. Findings The proposed method was applied to the AUBO-i5 robot to evaluate its performance. Simulation results demonstrate that CISSA effectively avoids local optima and achieves more accurate solutions compared to similar algorithms. By integrating CISSA into trajectory planning, the robot's movement time was reduced by 13.99% compared to the original SSA, and the number of algorithm iterations was significantly decreased, ensuring smoother and more efficient task execution in real production. Originality/value A CISSA is proposed and applied to the optimal time trajectory planning of the manipulator, verifying the effectiveness and superiority of the algorithm. Experimental results show that CISSA outperforms comparable algorithms by several orders of magnitude in solving manipulator inverse kinematics, significantly enhancing planning efficiency and reducing trajectory planning time.

关键词： Kinematics 3-5-3 Polynomials sparrow search algorithm Time-optimal trajectory planning

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Economic-environmental dispatch of isolated microgrids based on dynamic classification sparrow search algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Xie, Guodong Zhang, Mengjian Wang, Deguang Yang, Ming Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Pollutant emissions cause environmental harm and the volatility of renewable energy threatens the safe operation of a microgrid. To enhance the reliability of electricity supply while balancing environmental and economic goals, this study proposes a novel dynamic classification sparrow search algorithm (DSSA) to address the economic-environmental dispatch problem in isolated microgrids. In DSSA, an elite opposition-Chebyshev initialization strategy is used to balance the diversity and uniformity of initial solutions. Inspired by the principle of tailoring education to individual aptitudes, DSSA dynamically divides the sparrow population into three groups, with each group employing an appropriate strategy to improve convergence speed and accuracy. The convergence of DSSA is proved based on Markov chain theory. Finally, DSSA is benchmarked against six state-of-the-art swarm intelligence algorithms across two scenarios in a case study, achieving cost reductions of up to 2.10% and 2.18%, as well as CO2 emission reductions of up to 1.88% and 2.40%, respectively. Ablation and exploration-exploitation analyses confirm a 10% increase in the exploration capacity of DSSA. Parameter sensitivity tests evaluate the rationality of parameter choices. Additionally, statistical significance analysis assesses the performance differences between DSSA and other algorithms, with results indicating the superiority of DSSA in solving the economic-environmental dispatch problem of isolated microgrids.

关键词： Isolated microgrid Economic-environmental dispatch Carbon capture sparrow search algorithm Multi-subpopulation dynamic classification

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Dynamic step opposition-based learning sparrow search algorithm for UAV path planning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第1期28卷 1-15页

作者： He, Yong Wang, Mingran Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Hunan Peoples R China

In this paper, aiming at the problems of large randomness, low convergence accuracy, and easy falling into local optimum in the application of sparrow search algorithm to UAV three-dimensional path planning, a dynamic step opposition-based learning sparrow search algorithm is proposed. The algorithm first uses a good point set in the population initialization phase to improve the quality of the initial solution;secondly, the piecewise dynamic step size is used to optimize the update formula of the discoverer, and the extensive search is carried out in the early stage of the iteration. In the later stage, the known area is mined as much as possible to improve the search accuracy and convergence speed of the algorithm. Then, the crazy operator is integrated to optimize the predator update formula and improve the local search ability. Finally, t-distribution opposition-based learning is used to prevent the algorithm from falling into the local optimum. In this paper, the effectiveness of the improved algorithm is verified by six test functions and applied to the three-dimensional path planning of UAVs. The experimental results show that the proposed algorithm has a faster convergence speed than the traditional algorithm, and the planned path is shorter.

关键词： sparrow search algorithm Path planning Good point set Opposition-based learning

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Improving carbon dioxide emission predictions through a hybrid model utilising an advanced sparrow search algorithm

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ENVIRONMENTAL TECHNOLOGY 2025年 1-16页

作者： Ma, Si-yuan Wang, Xiao-kang Cheng, Sijia Liu, Ye Wang, Ya-nan Wang, Jian-qiang Cent South Univ Sch Business Changsha Peoples R China Shenzhen Univ Coll Management Shenzhen Peoples R China GuangXi Univ Finance & Econ Sch Accounting & Audit Nanning 530003 Peoples R China

The dramatic increase in carbon dioxide emissions is a major cause of global warming and climate change, posing a serious threat to human development and profoundly affecting the global ecosystem. Currently, carbon dioxide emissions prediction studies rely heavily on a large amount of data support, and the accuracy of predictions is greatly reduced when data are scarce. In addition, the inherent uncertainty, volatility, and complexity of CO2 emission data further exacerbate the challenge of accurate prediction. To address these issues, a novel hybrid model for CO2 emission prediction is proposed in this paper. A feature screening method is designed for effective and reliable feature selection from the perspective of algorithm stability, which can improve the prediction performance. In order to accurately predict periodic sequences with limited training samples, a least squares support vector machine is employed in this paper. In addition, the parameters of the prediction model are optimised using the improved sparrow search algorithm and enhanced by Sin chaos mapping, adaptive inertia weights and Cauchy-Gauss variables. An empirical study is conducted using Chinese carbon emission data as a case study, and the validity and superiority of the proposed model are verified through comparative experiments. The results show that the improved SSA has stronger global optimisation capability and faster convergence speed. In addition, in terms of prediction results, the hybrid model has the best consistency with the actual data, which significantly improves the prediction accuracy.

关键词： Carbon dioxide emissions forecasting Random forest Null importance Least squares support vector machine sparrow search algorithm

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A novel sparrow search algorithm based co-correlation graph construction strategy for wind turbine group anomaly identification via graph attention networks

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 260卷

作者： Wang, Xiaomin Zhuang, Xiao Zhou, Di Ge, Jian Xiang, Jiawei Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China

Anomaly identification by using Supervisory Control and Data Acquisition (SCADA) data is an important means to improve the reliability of wind turbine group (WTG) operation. However, due to the low reliability of SCADA systems, anomalies in the data itself may occur as a result of sensor failures or data transmission errors. The anomalies in the data itself will reduce the accuracy and reliability of WTG anomaly identification. In this paper, a sparrow search algorithm (SSA) based co-correlation graph (CG) construction strategy using graph attention networks (SSACG-GAT) is proposed for WTG anomaly identification. First, the adjacency matrix representing the correlation of sample parameters is constructed by taking the monitoring parameters as nodes and the correlation between parameters as edges. Second, the proposed SSAGC strategy is used to obtain a co-correlation graph by fusing the adjacency matrices calculated by different correlation analysis models. In the proposed SSAGC strategy, the SSA is used to obtain the optimal fusion weights of the different adjacency matrices. Finally, the obtained optimal co-correlation graph is input into the GAT network for WTG anomaly identification. Nine models are selected as benchmarks to validate the effectiveness and superiority of the proposed SSACG-GAT. The experimental results show that the proposed SSACG-GAT has the best identification performance compared with nine benchmark methods. In addition, the ablation experiment results also demonstrate that the proposed SSACG strategy can effectively improve the accuracy and reliability of WTG anomaly identification.

关键词： SCADA sparrow search algorithm Co-correlation graph Graph attention networks Anomaly identification

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The Modified sparrow search algorithm with Brown Motion and Levy Flight Strategy for the Class Integration Test Order Generation Problem

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BIOMIMETICS 2025年第4期10卷 195-195页

作者： Jiao, Chongyang Zhou, Qinglei Zhang, Wenning Zhang, Chunyan Informat Engn Univ Lab Adv Comp & Intelligence Engn Zhengzhou 450001 Peoples R China Zhengzhou Vocat Coll Ind Safety Henan Informat Engn Sch Zhengzhou 450000 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Software Coll Zhengzhou 450007 Peoples R China

Software testing identifies potential errors and defects in software. A crucial component of software testing is integration testing, and the generation of class integration test orders (CITOs) is a critical topic in integration testing. The research shows that search-based algorithms can solve this problem effectively. As a novel search-based algorithm, the sparrow search algorithm (SSA) is good at finding the optimal to optimization problems, but it has drawbacks like weak population variety later on and the tendency to easily fall into the local optimum. To overcome its shortcomings, a modified sparrow search algorithm (MSSA) is developed and applied to the CITO generation issue. The algorithm is initialized with a good point set strategy, which distributes the sparrows evenly in the solution space. Then, the discoverer learning strategy of Brownian motion is introduced and the Levy flight is utilized to renew the positions of the followers, which balances the global search and local search of the algorithm. Finally, the optimal solution is subjected to random wandering to increase the probability of the algorithm jumping out of the local optimum. Using the overall stubbing complexity as a fitness function to evaluate different class test sequences, experiments are conducted on open-source Java systems, and the experimental results demonstrate that the MSSA generates test orders with lower stubbing cost in a shorter time than other novel intelligent algorithms. The superiority of the proposed algorithm is verified by five evaluation indexes: the overall stubbing complexity, attribute complexity, method complexity, convergence speed, and running time. The MSSA has shown significant advantages over the BSSA in all aspects. Among the nine systems, the total overall stubbing complexity of the MSSA is 13.776% lower than that of the BSSA. Total time is reduced by 23.814 s.

关键词： integration testing test order sparrow search algorithm stubbing complexity Brownian motion Levy flight

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Infrared Image Segmentation Method Based on Variable Helix Optimized-sparrow search algorithm

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第2期32卷 657-667页

作者： Huang, JiLan Yang, Xing Jin, ZhiXiong Geely Univ China Chengdu 641423 Peoples R China

This study proposes an infrared image segmentation method based on a Variable Helix Optimized-sparrow search algorithm (VHO-SSA) to address the limitations of traditional image segmentation algorithms. The proposed method combines the Otsu threshold algorithm with an optimized sparrow search algorithm, which utilizes a variable helix position search method and a best point set method. The performance of the proposed method is evaluated using several benchmark functions and compared with other state-of-the-art algorithms. The results demonstrate that the VHO-SSA achieves high segmentation accuracy (up to 0.98) and maintains a high structural similarity index (above 0.90) in the presence of noise. The proposed method shows promise for improving the segmentation of infrared images in various applications.

关键词： division image otsu sparrow search algorithm threshold variable helix

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A novel inversion approach for seepage parameter of concrete face rockfill dams based on an enhanced sparrow search algorithm

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COMPUTERS AND GEOTECHNICS 2025年 183卷

作者： Feng, Yaxin Tian, Bin Shen, Zhenzhong Sun, Yiqing Zhang, Hongwei Xu, Liqun Gan, Lei Wang, Runying Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China Hohai Univ Coll Civil & Transportat Engn Nanjing 210098 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China

In order to rapidly and accurately determine the dam body and foundation permeability coefficients of concrete face rockfill dam, an improved sparrow search algorithm and the Support Vector Machine are combined to propose an ISSA-SVR model. In the ISSA, the Circle mapping initialization population position, periodic convergence factor, and Levy flight are employed to deal with uneven initial population distribution and easily fall into local optimum in the traditional sparrow search algorithm. The effectiveness of the proposed ISSA is verified by six classical test functions. The ISSA has been improved in terms of convergence speed, convergence accuracy, and stability. The simple seepage calculation of the earth-rock dam section proves that the improved ISSA-SVR model has smaller discreteness and higher accuracy than the SSA-SVR model and SVR model in parameter inversion. The ISSA-SVR model is applied in a practical project, and the relative error between the calculated value and the measured value of the water head at each measuring point is within 5 %. The seepage field distribution of the dam body is investigated, and the anti-seepage system blocks 91.9 % of the total water head. The anti-seepage sensitivity analysis of the peripheral joint shows that when the width of the joint reaches 20 mm, the penetration gradient of the cushion exceeds its allowable value. The research results confirm the superiority of the inversion model in the inversion of seepage parameters.

关键词： Permeability coefficient Surrogate model sparrow search algorithm Seepage calculation Inversion analysis Peripheral joint

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Structural Damage Detection Based on sparrow search algorithm

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BUILDINGS 2025年第7期15卷 1179-1179页

作者： Ma, Ziyan Peng, Xi Qin, Fengjiang Yang, Qiuwei Xu, Bin Ningbo Univ Technol Sch Civil & Transportat Engn Ningbo 315211 Peoples R China Ningbo Univ Technol Engn Res Ctr Ind Construction Civil Engn Zhejiang Ningbo 315211 Peoples R China Ningbo Roaby Technol Ind Grp Co Ltd Ningbo 315800 Peoples R China Chongqing Univ Sch Civil Engn Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China

Vibration modal parameters are widely used in structural damage identification due to their ease of measurement, facilitating rapid structural damage assessment in engineering practice. However, traditional vibration-based methods impose stringent requirements on data volume and accuracy, leaving room for improvement in computational efficiency and precision. To address this issue, a damage identification method combining the sparrow search algorithm (SSA) and natural frequency sensitivity analysis is proposed. This method employs the SSA to solve the linear equations of frequency sensitivity to obtain structural damage parameters, thereby enabling structural damage assessment. The advantages of the proposed method are as follows: firstly, the SSA, compared to other swarm intelligence algorithms, can more accurately solve for damage parameters;secondly, sensitivity analysis is used to predefine the search area, thereby enhancing computational efficiency;furthermore, a conversion formula is applied to enhance computational accuracy in cases of significant damage. Three numerical cases and two experimental examples are used to validate the proposed algorithm, which is also compared with other swarm intelligence algorithms. The research results indicate that this method has significant advantages in locating damage and accurately assessing damage severity, with minimal misjudgment of undamaged units. The calculated damage parameters are closer to the true values compared to those obtained by other methods.

关键词： natural frequency damage detection sparrow search algorithm sensitivity objective function

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