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Intelligent Path Planning with an Improved sparrow search algorithm for Workshop UAV Inspection

SENSORS 2024年第4期24卷 1104页

作者： Zhang, Jinwei Zhu, Xijing Li, Jing North Univ China Sch Mech Engn Taiyuan 030051 Peoples R China North Univ China Shanxi Prov Key Lab Adv Mfg Technol Taiyuan 030051 Peoples R China

Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA's search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping-firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms.

关键词： UAV sparrow search algorithm firefly algorithm chaotic sequence path planning

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sparrow search algorithm Based on Cubic Mapping and Its Application 19th

Sparrow Search Algorithm Based on Cubic Mapping and Its Appl...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Zheng, Shuo Zou, Feng Chen, DeBao Huaibei Normal Univ Huaibei 235000 Peoples R China

ISBN: (纸本)9789819947546;9789819947553

sparrow search algorithm (SSA) easily appears some problems such as falling into local optima and lacking search precision. To address these issues, a sparrow search algorithm based on cubic mapping (CSSA) improvement is proposed. Wanting a better population, a Cubic chaotic initialization method is used to improve population quality. During the producer position update process, levy fight is added to widen the search area of the algorithm. Then, an inertia weight is introduced during the scrounger phase. The algorithm is tested using CEC2022 test functions and compared with multiple algorithms. The data show that CSSA can overcome the problem that SSA easily getting stuck in local optimum to some extent, and improve convergence accuracy and stability. At last, CSSA is applied to an engineering problem of three-bar truss, and compared with other algorithms in a comparative experiment.

关键词： sparrow search algorithm Cubic Levy fight Three-bar truss

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Urban Flood Prediction Model Based on Transformer-LSTM-sparrow search algorithm

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WATER 2025年第9期17卷 1404-1404页

作者： Fan, Zixuan Zhang, Jinping Chen, Yanpo Xu, Hongshi Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Zhengzhou Municipal Facil Affairs Ctr Zhengzhou 450052 Peoples R China

Global climate change and accelerated urbanization have intensified extreme rainfall events, exacerbating urban flood risks. Although data-driven models have shown potential in urban flood prediction, the ability of single models to capture complex nonlinear relationships and their sensitivity to hyperparameters still limit prediction accuracy. To address these challenges, this study proposes an urban flood prediction model by integrating Transformer, Long Short-Term Memory (LSTM), and sparrow search algorithm (SSA), combining Transformer's global feature extraction with LSTM's temporal modeling. The SSA was adopted to optimize hyperparameters for the Transformer-LSTM model. Dropout and early stopping techniques were adopted to mitigate overfitting. Applied to Zhengzhou city of Henan province, China, the model achieves a Nash-Sutcliffe Efficiency (NSE) of 0.971, indicating that the proposed model has high prediction performance for urban flooding. The experimental results demonstrate that the Transformer-LSTM-SSA model outperforms the standalone Transformer, LSTM, and Transformer-LSTM models by 12.9%, 10.1%, and 2.9% in NSE accuracy, respectively, while reducing MAE by 62.12%, 56.9%, and 34.21%, respectively, and MAPE by 21.69%, 22.2%, and 10.89%, respectively. Furthermore, the proposed model exhibits enhanced stability and superior generalization capability. The Transformer-LSTM-SSA model exhibits superior performance among the comparative methods, thereby demonstrating the model's viability for providing a reliable solution for real-time flood prediction and early warning.

关键词： urban flood data-driven models long short-term memory transformer sparrow search algorithm

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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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A novel sparrow search algorithm with integrates spawning strategy

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1753-1773页

作者： Zhang, Jiaxiang Li, Linwei Zhang, Huanlong Wang, Fengxian Tian, Yangyang Zhengzhou Univ Light Ind Coll Elect & Informat Engn 136 Ke Xue Ave Zhengzhou 450000 Henan Peoples R China State Grid Henan Elect Res Inst 85 Songshan South Rd Zhengzhou 450000 Henan Peoples R China

sparrow search algorithm (SSA) has recently received more attention in the intelligent optimization group. However, the high randomness and local optimal problems of the algorithm limit the application of the algorithm. In this paper, a novel sparrow search algorithm (NSSA) combined with spawning technology is proposed, which effectively improves the problem of the algorithm falling into local optimization. First of all, it is proposed to replace the traditional stochastic method with the good point set theory to find the initial individual, so that the initial population is more evenly distributed in the search space and improve the quality of the initial solution. Secondly, the spawning strategy of the cuckoo algorithm is integrated into the discoverer stage, which improves the search method of the discoverer and enhances the global search ability, which makes the algorithm avoids the sudden decline of population diversity and premature convergence. Finally, Levy flight and Brownian motion are used to disturb the position of the sparrow dimension by dimension to improve the ability of the algorithm to jump out of the local optimal value in subsequent iterations. By comparing and analyzing NSSA and SSA, DE, ALO, GOA, DOA, HSSA and XSSA algorithms in 12 common evaluation functions and CEC-2017 test functions, and apply NSSA to the TSP problem. The simulation results show the effectiveness and superiority of the proposed scheme.

关键词： sparrow search algorithm Optimization scheme Cuckoo algorithm Local optimum problem High randomicity

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A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved sparrow search algorithm-Particle Swarm Optimization algorithm

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ENERGIES 2024年第9期17卷 2001页

作者： Wang, Yonggang Li, Fuxian Xiao, Ruimin Zhang, Nannan Shenyang Agr Univ Coll Informat & Elect Engn Shenyang 110866 Peoples R China State Grid Huludao Elect Power Supply Co Huludao 125000 Peoples R China

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm-particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

关键词： multi-objective reactive power optimization network loss sparrow search algorithm particle swarm optimization

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Optimization of PID controller for stepper motor speed control system based on improved sparrow search algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第19期238卷 9397-9411页

作者： Zhang, Mingfeng Xu, Chuntian Li, Lin Wang, Zihuimin Zong, Xu Univ Sci & Technol Liaoning Sch Mech Engn & Automat Anshan Peoples R China Jinan Heavy Ind Grp Co LTD Intelligent Roadheading Technol & Assembly Res Ins Jinan Peoples R China Univ Nottingham Ningbo China Fac Sci & Engn Ningbo Peoples R China Angang Steel Co LTD Color drawing factory Anshan Iron & Steel Anshan Peoples R China

To improve the control accuracy of the stepper motor, a PID controller based on an improved sparrow search algorithm (ISSA-PID) is designed to improve the response speed as well as the robustness of the closed-loop speed control of the stepper motor by optimizing the position update formula and the step size control parameters based on the sparrow search algorithm. ISSA-PID is applied to the stepper motor speed control system in Matlab and tested by Ramp-up Load and applying perturbation simulation respectively. By comparing with traditional PID, Differential Evolution algorithm PID controller, Particle Swarm Optimization algorithm PID controller, and Ant Lion Optimization algorithm PID controller, the results show that ISSA-PID not only improves the convergence speed and accuracy but also performs better in terms of stability. Finally, the experimental platform of the stepper motor speed control system is built to experimentally verify the performance of the ISSA-PID controller, and the experimental results show that ISSA-PID has stronger robustness and faster response speed compared with the remaining four controllers.

关键词： sparrow search algorithm stepping motor speed control system PID controller parameter optimization

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Optimization of Deep Belief Network Based on sparrow search algorithm for Rolling Bearing Fault Diagnosis

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IEEE ACCESS 2024年 12卷 10470-10481页

作者： Xu, Donghao Li, Cheng Shanghai Univ Engn Sci Coll Air Transportat Shanghai 201620 Peoples R China

This study addresses the randomness of training parameters in the Deep Belief Network (DBN) and proposes an optimization method for rolling bearing fault diagnosis based on the sparrow search algorithm (SSA). SSA is employed to globally optimize the structural and training parameters of the DBN network, effectively resolving the challenge of parameter determination. Simultaneously, vibration signals are extracted from multiple dimensions to capture different types of fault features. These features are derived through Wavelet Transformation (WT) for noise reduction and Intrinsic Mode Functions (IMFs) extraction through Ensemble Empirical Mode Decomposition (EEMD). The fusion of time-domain and frequency-domain dimensional features forms a multidimensional feature set. This comprehensive feature set optimizes the parameters of the deep learning network and significantly improves the accuracy and effectiveness of rolling bearing fault diagnosis. With a remarkable recognition accuracy of 99.17%, this approach outperforms conventional feature sets and mainstream diagnostic methods such as PSO-DBN and SSA-SVM while maintaining high levels of generalization and stability. The introduction of this method represents a significant breakthrough in the field of rolling bearing fault diagnosis.

关键词： Deep belief network fault diagnosis multi-domain features sparrow search algorithm

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