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Improved sparrow search algorithm with adaptive multi-strategy hierarchical mechanism for global optimization and engineering problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-44页

作者： Wei, Fengtao Feng, Yue Shi, Xin Hou, Kai Xian Univ Technol Sch Mech & Instrumental Engn Xian 710048 Peoples R China

Aiming at the problem that the sparrow search algorithm (SSA) does not have high optimization accuracy and is prone to fall into local optimum, an improved sparrow search algorithm with adaptive multi-strategy hierarchical mechanism is proposed (ISSA). Firstly, in the initialization phase, the population is created by combining the triangular topology and Logistic Chaos mapping, and an elite dynamic reverse learning strategy is used to enhance the population diversity and balance the local and global search performance. Secondly, an adaptive multi-strategy hierarchical mechanism is applied to the population, where adaptive dynamic adjustment strategy is applied to the discoverers to improve the flexibility and search efficiency of the algorithm;differential mutation operation is applied to the followers to generate a mutated subpopulation, which enhances the ability of the algorithm to jump out of the local optimum;and vertical and horizontal crossover strategies are applied to the vigilantes, where horizontal crossover enhances the global search ability, and vertical crossover maintains the population diversity and prevents the algorithm from falling into local optimality. Finally, the classical benchmark functions as well as the CEC2020 and CEC2022 test functions are selected for simulation and analysis, and the ISSA is compared with other optimization algorithm, and the ANOVA analysis, the Wilcoxon rank-sum test, and the Friedman test are performed. The simulation results show that the ISSA proposed in this paper achieves significant improvement in both convergence accuracy and convergence speed. Meanwhile, the application of ISSA to engineering problems fully verifies its practical value and significant advantages in the field of engineering problems.

关键词： sparrow search algorithm Triangular chaotic inverse learning initialization strategy Adaptive dynamic adjustment Mutation operator Vertical and horizontal crossover strategy Engineering problems

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Path tracking control of crawler tractor based on adaptive adjustment of lookahead distance using sparrow search algorithm☆

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 234卷

作者： Song, Yue Xue, Jinlin Zhang, Tianyu Sun, Xiaoxu Sun, Han Gao, Weiwei Chen, Qiqi Nanjing Agr Univ Coll Engn Nanjing 210031 Jiangsu Peoples R China

The complex and changeable farmland environment brings great challenges to the path tracking and stable control of autonomous agricultural machinery. To improve the efficiency and stability of path tracking control for autonomous agricultural machinery before entering the path from the headland and after entering the path, a path tracking control method for crawler tractor is proposed which can adjust the lookahead distance adaptively in Pure Pursuit control by using sparrow search algorithm. In the method, the lookahead distance is regarded as sparrow individuals, and then the optimal target point is selected by simulating natural selection. Here, two lookahead area delineation schemes are set in light of the current lateral deviation of the agricultural machinery, and the lookahead area is preprocessed before searching the optimal target point to improve the efficiency and performance of the algorithm. Then, the current position information is substituted into the tracking control model of crawler tractor, and the position at the next moment is predicted under different lookahead distances. The predicted position is compared with the preset path to obtain the axial deviation and the heading deviation at the next moment. Additionally, the fitness function is constructed with the prediction deviation as the variable, and the optimal target point in the lookahead area can be searched by comparing the fitness values. In order to verify the feasibility of the proposed method, the ''entry-line and straight-line' tracking test of agricultural machinery under different initial postures and the curve tracking test with time-varying curvature were carried out on the platform of tracked tractor. The test results show that the average value, maximum value and standard deviation of the lateral deviation and the heading deviation after the agricultural machinery reaches the stable tracking state are not more than 0.030 m, 0.106 m, 0.034 m, 0.275 degrees, 1.528 degrees, 0.321 de

关键词： Path tracking Lookahead distance Adaptive adjustment sparrow search algorithm Crawler tractor

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Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control for delta robot based on parameter optimization of sparrow search algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年

作者： Liu, Jian Guo, Zhongfeng Yang, Junlin Shenyang Univ Technol 111 Shenliao West RdEcon & Technol Dev Zone Shenyang 110870 Peoples R China

Aiming at the characteristics of nonlinearity, strong coupling and unknown perturbation in the dynamics model of Delta robot under high-speed operation, this paper designs a new Global Non-singular Fast Terminal Sliding Mode Control (GNFTSMC), which avoids the singularity of the traditional terminal sliding mode control. On this basis, Fuzzy Global Non-singular Fast Terminal Sliding Mode Control (FGNFTSMC) is proposed by combining Global Non-singular Fast Terminal Sliding Mode Control with fuzzy system, which is a method of eliminating the chattering of sliding mode controller by using fuzzy logic outputs instead of the reaching control law. To improve the trajectory tracking accuracy, the Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control (AFGNFTSMC) is proposed by adjusting output gain online through the adaptive fuzzy system. The sparrow search algorithm (SSA) is proposed to optimize the Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control (AFGNFTSMC) sliding surface parameters and select the optimal sliding surface parameters. Experiments show that applying the Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control optimized based on the SSA (SSA-AFGNFTSMC) to the Delta robot improves the tracking accuracy, reduces the convergence time and eliminates the controller output chattering.

关键词： Delta robots Global Non-singular Fast Terminal Sliding Mode adaptive fuzzy system sparrow search algorithm chattering reduction

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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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An improved sparrow search algorithm with multi-strategy integration

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SCIENTIFIC REPORTS 2025年第1期15卷 1-28页

作者： Wang, Zongyao Peng, Qiyang Rao, Wei Li, Dan Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China Fudan Univ Sch Informat Sci & Technol Key Lab Informat Sci Electromagnet Waves Shanghai 200433 Peoples R China Fudan Univ Res Ctr Smart Networks & Syst Sch Informat Sci & Technol Shanghai 200433 Peoples R China

Addressing the shortcomings of the sparrow search algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated sparrow search algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer's position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the Coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum. Finally, a proposed random opposition-based learning strategy (ROBLS) is applied to the population after each iteration to enhance its diversity. To verify MISSA's effectiveness, extensive testing is conducted on 24 benchmark functions as well as CEC 2017 functions. The experimental results, complemented by Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA and other advanced optimization algorithms, exhibiting superior overall performance.

关键词： sparrow search algorithm Multi-strategy Black-winged kite algorithm Coot algorithm Opposition-based learning strategy

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Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals

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AUTOMATIKA 2025年第2期66卷 321-334页

作者： Ling, Liuyi Lin, Zhu Feng, Bin Wei, Liyu Jin, Li Wang, Yiwen Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Artificial Intelligence Huainan Peoples R China Anhui Univ Sci & Technol Inst Environm Friendly Mat & Occupat Hlth Wuhu Peoples R China

Continuous prediction of human joint angles is crucial for enhancing the performance of man-machine cooperative control. This study proposed a random forest (RF) model optimized by the sparrow search algorithm (SSA) for achieving continuous motion prediction of the lower limb knee joint based on surface electromyography(sEMG) under various motion modes. The sEMG signal and knee angle were recorded during four human motions, namely normal gait, sitting-standing transition, ascending stairs and descending stairs. These signals were processed to remove noise and extract eigenvalues in both time and frequency domains. A knee angle prediction model based on SSA-RF algorithm was trained and evaluated using feature sample data. In the four motion mode experiments, the SSA-RF model achieved a minimum root-mean-square error of 1.569 degrees for predicting knee joint angle, the average absolute error was only 1.05 degrees, and the coefficient of determination was as high as 0.99. The performance of the proposed model was compared with those of traditional backpropagation neural network, support vector machine regression, and random forest models. The comparison results clearly indicate that the proposed model can more effectively predict knee joint angles under various motion modes and performs better in promoting human-machine cooperation.

关键词： Surface electromyography signal continuous motion prediction sparrow search algorithm random forest knee joint

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Chaos and elite reverse learning - Enhanced sparrow search algorithm for IIoT sensing communication optimization

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ALEXANDRIA ENGINEERING JOURNAL 2025年 125卷 663-676页

作者： Wang, Yongmei Li, Junyong Tan, Xiaoyun Hefei Normal Univ Sch Comp & Artificial Intelligence Hefei 230601 Peoples R China Jiangxi Inst Fash Technol Business Sch Nanchang 330201 Peoples R China

Sensing, communication, and collaborative optimization are currently hot topics in the Industrial Internet of Things (IIoT) research. This paper addresses minimizing energy consumption in IIoT user terminal devices by modeling energy consumption as an optimization challenge. Initially, a data-aware sharing architecture for IIoT user terminal devices is constructed to reduce device energy consumption. In scenarios involving multiple intelligent terminal devices, collaborative devices, and edge IIoT proxy devices, factors such as user device location stability, local network status, task arrival rate, and queue stability are comprehensively considered. Subsequently, this paper introduces a Chaos and Elite Reverse Learning sparrow search algorithm (CERL-SSA) to solve the established model. The testing experiments use common benchmark functions to verify the superiority of the improved algorithm, and the experimental results show the good performance and effectiveness of the proposed algorithm in IIoT sensing communication and collaborative optimization.

关键词： Industrial Internet of Things Sensory communication Cooperative optimization Chaos and elite inverse learning sparrow search algorithm

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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

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Energy Engineering 2025年第1期122卷 203-220页

作者： Yu Zhang Daoyu Zhang TiezhouWu School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China

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 ***,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity ***,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 *** 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 ***,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity ***,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 *** validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging *** 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%.

关键词： Lithium-ion battery state of health differential thermal voltammetry sparrow search algorithm

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Research on Load Forecasting Based on Deep Learning Framework Optimized by sparrow search algorithm

Research on Load Forecasting Based on Deep Learning Framewor...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Zeng, Zhaoyu Ni, Fuyin School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China Jiangsu University of Technology Jiangsu Key Laboratory for Power Transmission & Distribution Equipment Technology Changzhou213001 China

ISBN: (纸本)9798331506797

Precise power load forecasting is crucial for maintaining the stable and efficient operation of power systems. To enhance predicted accuracy, this study introduces a hybrid forecasting approach that integrates sparrow search algorithm (SSA) with deep learning framework. Convolutional neural network (CNN) is employed to capture nonlinear relationships among factors influencing power load, generating high-quality feature representations. Then the sparrow search algorithm (SSA) optimizes the parameters of the Long Short-Term Memory (LSTM) to enhance its fitting ability to handle raw load data. Subsequently, the LSTM models the time-dependence of power loads and accurately captures the short-term fluctuation characteristics. Finally, the Attention mechanism is incorporated to assign weights to the input features, emphasizing critical features and improving the prediction accuracy. The experimental findings indicate that, compared with alternative models, the proposed CNN-SSA-LSTM-Attention model attains an $\mathrm{R}^{2}$ value of 0.983, with the MAPE significantly decreased to 2.85%. In addition, the RMSE and MAE are reduced to 2.94kW and 2.12kW, respectively, which are better than other models. © 2025 IEEE.

关键词： Attention Mechanism Load Forecasting Neural Network sparrow search algorithm

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Path planning for mobile robots in complex environments based on enhanced sparrow search algorithm and dynamic window approach

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ROBOTICA 2025年

作者： Luo, Yixuan Lin, Shusen Wang, Yifan Liang, Kai Taizhou Univ Coll Intelligent Mfg Taizhou 318001 Peoples R China

Traditional path planning algorithms often encounter challenges in complex dynamic environments, including local optima, excessive path lengths, and inadequate dynamic obstacle avoidance. Thus, the development of innovative path planning algorithms is essential. This article addresses the challenges of mobile robot path planning in complex environments, where traditional methods often converge to local optima, leading to suboptimal path lengths, and struggle with dynamic obstacle avoidance. To overcome these limitations, we propose an integrated algorithm, the enhanced sparrow search algorithm combined with the dynamic window approach (ESSA-DWA). The algorithm first utilizes ESSA for global path planning, followed by local path planning facilitated by the DWA. Specifically, ESSA incorporates Tent chaotic initialization to enhance population diversity, effectively mitigating the risk of premature convergence to local optima. Moreover, dynamic adjustments to the inertia weight during the search process enable an adaptive balance between exploration and exploitation. The integration of a local search strategy further refines individual updates, thereby improving local search performance. To enhance path smoothness, the Floyd algorithm is employed for path optimization, ensuring a more continuous trajectory. Finally, the combination of ESSA and DWA uses key nodes from the global path generated by ESSA as reference points for the local planning process of DWA. This approach ensures that the local path closely follows the global path while also enabling real-time dynamic obstacle detection and avoidance. The effectiveness of the algorithm has been validated through both simulations and practical experiments, offering an efficient and viable solution to the path planning problem.

关键词： mobile robot sparrow search algorithm dynamic window approach path planning dynamic obstacle avoidance

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