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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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sparrow search algorithm-driven clustering analysis of rock mass discontinuity sets

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COMPUTATIONAL GEOSCIENCES 2024年第4期28卷 615-627页

作者： Wu, Wenxuan Feng, Wenkai Yi, Xiaoyu Zhao, Jiachen Zhou, Yongjian Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China

Rock discontinuity has a crucial impact on the deformation and strength of rock masses, and thus, the clustering of discontinuities is a critical aspect of rock mechanics. Traditional clustering methods require initial cluster centers to be specified and involve a multitude of parameter calculations, leading to a complex and cumbersome process. In this paper, a novel clustering approach based on the sparrow search algorithm (SSA) is introduced to overcome these limitations. This method utilizes a sparrow population coding technique and fitness function tailored to the unique characteristics of rock discontinuity orientation data. The SSA is adeptly applied to the clustering of rock joints, and the optimal number of clusters are automatically determined via the silhouette coefficient method. This methodology was tested on artificial datasets and actual discontinuity survey results from the underground powerhouse of the Henan Wuyue Hydropower Station to evaluate its feasibility and efficacy in analyzing rock discontinuities. Comparative data analysis reveals that the proposed method outperforms classic algorithms such as FCM and KPSO in terms of clustering accuracy and stability. The proposed method stands out among various clustering methods of discontinuity orientation for its ability to achieve convergent results without user intervention, demonstrating significant practical utility.

关键词： Rock discontinuity Clustering analysis sparrow search algorithm Silhouette coefficient

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sparrow search algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification

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IEEE ACCESS 2023年 11卷 111927-111935页

作者： Ramesh, Janjhyam Venkata Naga Abirami, T. Gopalakrishnan, T. Narayanasamy, Kanagaraj Ishak, Mohamad Khairi Karim, Faten Khalid Mostafa, Samih M. Allakany, Alaa Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada 522502 Andhra Pradesh India Kongu Engn Coll Dept Informat Technol Erode 638060 India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Informat Technol Bengaluru 576104 India Karpagam Acad Higher Educ Dept Comp Sci Coimbatore 641021 India Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Malaysia Ajman Univ Dept Elect & Comp Engn Ajman U Arab Emirates Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia South Valley Univ Fac Comp & Informat Comp Sci Dept Qena 83523 Egypt New Assiut Technol Univ NATU Fac Ind & Energy Technol New Asyut 71515 Egypt Kafrelsheikh Univ Fac Comp & Informat Kafr El Shaikh 6860404 Egypt

Medical image analysis for pancreatic cancer (PC) classification and recognition is a vital domain of research and medical practices. PC is challenging to diagnose and treat;medical imaging approaches aid early diagnosis to analyse and treat, and employ of medical imaging approaches are support early diagnosis, correct analysis, and treatment planning. Computed Tomography (CT) scans are generally utilized to detect and classify PCs. Deep learning (DL) approaches have demonstrated the ability to support the diagnosis and detection of several medical conditions, containing PC. Convolutional Neural Networks (CNNs) are a kind of DL approach generally employed for image analysis that is trained to automatically learn and extract features in medical images. So, this study purposes a new sparrow search algorithm with Stacked Deep Learning based Medical Image Analysis for Pancreatic Cancer Detection and Classification (SSASDL-PCDC) technique on CT images. The purpose of the study is to design an SSASDL-PCDC technique to achieve improved pancreatic cancer detection performance. In addition, the SSASDL-PCDC technique applies Harris Hawks Optimization (HHO) with a densely connected networks (DenseNet) model for the feature extraction process. Moreover, convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM) approach was utilized for PC detection and classification. Furthermore, sparrow search algorithm (SSA) is used to adjust the hyperparameter values of the CNN-BiLSTM technique. To evaluate the effectiveness of the SSASDL-PCDC technique, extensive experiments were executed on a comprehensive database of pancreatic CT images. The simulation outcome value depicted that the SSASDL-PCDC technique with maximum sensitivity of 99.26%, specificity of 99.26%, and accuracy of 99.26%.

关键词： Deep learning Image analysis Computed tomography Pancreatic cancer Feature extraction Classification algorithms Convolutional neural networks computed tomography images sparrow search algorithm medical image analysis cancer diagnosis

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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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sparrow search algorithm applied to temperature control in PEM fuel cell systems

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第94期47卷 39973-39986页

作者： Xu, Jiang-Hai Yan, Han-Zhang Ding, Quan Zhu, Kai-Qi Yang, Yan-Ru Wang, Yu-Lin Huang, Tai-Ming Chen, Xi Wan, Zhong-Min Wang, Xiao-Dong North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Res Ctr Engn Thermophys Beijing 102206 Peoples R China Hunan Inst Sci & Technol Coll Mech Engn Yueyang 414006 Peoples R China Tianjin Univ Commerce Sch Mech Engn Tianjin 300134 Peoples R China

Working temperature is a critical parameter that influences the performance of proton exchange membrane fuel cells (PEMFCs). Appropriate thermal management can improve the efficiency of PEMFCs and prevent irreversible internal damage such as membrane degradation. To solve the slow response and poor dynamic performance of traditional temperature control strategies, the dynamic temperature mode of PEMFCs is established and a temperature control strategy based on the sparrow search algorithm-proportional integral differential (SSA-PID) is proposed in this study. The performance of the SSA-PID temperature control is verified by the simulations of current step change, vehicle dy-namic load, and working parameter variation. The results show that the proposed method has the advantages of fast convergence, better dynamic performance, and anti-disturbance ability. The maximum power density of the SSA-PID temperature controller is 6.58% and 12.94% higher than GA (genetic algorithm)-PID and traditional PID controllers, respectively. Moreover, the proposed method can ensure temperature fluctuations within 0.5 K under dynamic disturbance.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： PEMFC Temperature control sparrow search algorithm Thermal management

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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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