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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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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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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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A multi-mechanism balanced advanced learning sparrow search algorithm for UAV path planning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 6623-6666页

作者： Yang, Chao Yang, Hong Zhu, Donglin Hu, YiWen Zhang, Yu Ma, HongYuan Zhang, Di Shenyang Ligong Univ Coll Informat Sci & Engn Shenyang 110000 Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China

Unmanned aerial vehicle (UAV) is highly flexible and versatile, ranging from monitoring and surveying to rescue and military applications, but finding the best path requires a large amount of computing resources. Through intelligent path planning algorithms, UAV can find the best path according to task requirements and environmental conditions, avoid obstacles, and bypass dangerous areas, thereby effectively reducing the risks and errors of task execution. However, ordinary heuristic algorithms often do not achieve satisfactory results. To address this problem, A multi-mechanism balanced advanced sparrow search algorithm (BALSSA) is proposed. To achieve a balance between exploration and exploitation in the algorithm, we introduce two innovative techniques: an adaptive weight jumping mechanism and a suicide mutation perturbation. Then a balanced advanced learning is proposed. this approach enhances the evolutionary learning capabilities of SSA and aids the algorithm in timely escaping from local optima, and then propose a spiral factor improved progressive learning to improve the mutual learning performance between individuals, tested on 23 benchmark test functions, CEC2017 and CEC2022 The set is compared with algorithms in recent years and proposed algorithm variants, and the results show that BALSSA has better optimization and robustness. Finally, the proposed BALSSA is applied to UAV path planning. Compared with the variant SSA in recent years, BALSSA shows more stable and accurate optimization performance to verify the practicability of the improved algorithm.

关键词： sparrow search algorithm Adaptive Balanced advanced learning Spiral progressive learning UAV path planning

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Design of the sparrow search algorithm (SSA) for airborne radioactive hotspot detection

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APPLIED RADIATION AND ISOTOPES 2024年 209卷 111333页

作者： Xiong, Chao Qiao, Xin Xu, Jie Luo, Guang-hao Chen, Hao Fu, Zhen Luo, Boya Wu, Hexi East China Univ Technol Sch Nucl Sci & Engn Nanchang Peoples R China Inst Mil Civilian Integrat Jiangxi Prov Nanchang 330029 Peoples R China

In the context of using aircraft as a pivotal tool for detecting radioactive hotspots, the acquisition of radioactivity data was conducted through a CeBr3 scintillation crystal detector mounted on a helicopter. However, challenges arose, including managing extensive data volumes, computationally demanding tasks, and susceptibility to local optima issues. To address these challenges and leverage the benefits of the sparrow search algorithm (SSA) in global optimization and convergence speed, an improved SSA was devised. This improved version integrated SSA principles with the intricacies of searching for radioactive hotspots. The algorithm employed a matrix segmentation method to process data matrices derived from measured data, aiming to enhance efficiency and accuracy. An empirical analysis was conducted, performing 100 iterations on an experimental matrix to scrutinize the impact of matrix segmentation. Computation times and results were compared across different segmentation levels, confirming the favorable algorithmic outcomes of the method. The practical viability and convergence stability of the algorithm were further assessed using genuine measured data, with segmented matrices generated for evaluation. Remarkably, a comparison between computational outcomes and manually identified data reaffirmed the algorithm's reliability in effectively detecting radioactive hotspots.

关键词： Hotspot detection sparrow search algorithm Matrix segmentation

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Modeling and identification of hysteresis of marine damper considering shock environment based on evolutionary sparrow search algorithm

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OCEAN ENGINEERING 2024年第Part2期311卷

作者： Zhou, Yanfeng Chen, Weifang Wang, Dan Chen, Kaijie Zhu, Rupeng Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Natl Key Lab Sci & Technol Helicopter Transmiss Nanjing 210016 Peoples R China

The mechanical properties of marine dampers are usually highly nonlinear in order to adapt to dynamic and shock environments. The dampers are subjected to dynamic and shock tests and the hysteresis curves are found to be rate-dependent and asymmetric. To be able to describe the dynamic hysteresis and shock hysteresis, a ratedependent generalized Prandtl-Ishlinskii (RDGPI) model is proposed based on the generalized Prandtl-Ishlinskii (GPI) model, which is a hybrid of the GPI model and the radial basis function (RBF) neural network. Due to the large number of parameters in the model, thus aiming to improve the effect of parameter identification, an evolutionary sparrow search algorithm mixed with sparrow search algorithm and differential evolutionary algorithm is proposed. Compared with other algorithms, sparrow search algorithm is verified to have ideal optimization effect and convergence speed, and it is not easy to fall into the local optimum. Finally, the RDGPI model is experimentally verified, and the results show that the prediction error of the RDGPI model for dynamic hysteresis is within 0.06 kN, and the prediction error for shock hysteresis is within 0.8 kN, which is capable of describing the rate-dependent characteristics of the damper hysteresis. And it has a very good generalization ability, which makes it very suitable to be used in the modeling of the mechanical characteristics of the marine damper.

关键词： Marine damper Shock environment Prandtl - ishlinskii model Hysteresis sparrow search algorithm

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Adaptive B-spline curve fitting with minimal control points using an improved sparrow search algorithm for geometric modeling of aero-engine blades

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MULTIMEDIA SYSTEMS 2024年第5期30卷 1-16页

作者： Su, Chang Han, Yong Lu, Suihao Jiang, Dongsheng Ocean Univ China Dept Informat Sci & Engn Qingdao 266100 Peoples R China Qingdao Marine Sci & Technol Ctr Lab Reg Oceanog & Numer Modeling Qingdao Peoples R China Beijing Bytedance Technol Co Ltd Beijing 410000 Peoples R China AECC South Ind Co Ltd Zhuzhou City 410000 Hunan Province Peoples R China

In Industry 4.0 and advanced manufacturing, producing high-precision, complex products such as aero-engine blades involves sophisticated processes. Digital twin technology enables the creation of high-precision, real-time 3D models, optimizing manufacturing processes and improving product qualification rates. Establishing geometric models is crucial for effective digital twins. Traditional methods often fail to meet precision and efficiency demands. This paper proposes a fitting method based on an improved sparrow search algorithm (SSA) for high-precision curve fitting with minimal control points. This enhances modeling precision and efficiency, creating models suitable for digital twin environments and improving machining qualification rates. The SSA's position update function is enhanced, and an internal node vector update range prevents premature convergence and improves global search capabilities. Through automatic iterations, optimal control points are calculated using the least squares method. Fitness values, based on local and global errors, are iteratively calculated to achieve target accuracy. Validation with aero-engine blade data showed fitting accuracies of 1e-3 mm and 1e-5 mm. Efficiency in searching for minimal control points improved by 34.7%-49.6% compared to traditional methods. This SSA-based fitting method significantly advances geometric modeling precision and efficiency, addressing modern manufacturing challenges with high-quality, real-time production capabilities.

关键词： Digital twin B-spline curve fitting sparrow search algorithm Geometric modeling Control points optimization Aero-engine blade

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Machine Learning Surrogate Model Optimized by Improved sparrow search algorithm for Multi-Objective Optimization of Permanent Magnet Synchronous Motor Direct-Drive Pump

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 12773-12786页

作者： Zhang, Yiming Fei, Liangyu Chui, Chee-Kong Chng, Chin-Boon Zhao, Shengdun Li, Jingxiang Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore Xian Key Lab Intelligent Equipment & Control Xian 710049 Peoples R China

This study introduces an Improved sparrow search algorithm (ISSA) to address the challenges of machine learning Hyperparameter Optimization (HPO) and efficient modeling of Electric Coolant Pumps (ECP) in electric vehicles. By integrating L & eacute;vy flight and Bernoulli mapping, ISSA enhances global search capabilities and ability to escapes from local optima. Experimental validations across 12 benchmark functions demonstrate ISSA outperforming the standard sparrow search algorithm (SSA) and other advanced optimization algorithms in terms of exploratory and exploitative effectiveness. Specifically, ISSA proves exceptionally effective in autonomously handling the HPO for three typical Machine Learning (ML) algorithms, demonstrating superior performance over SSA and random search methods. A novel ISSA-ML surrogate model for ECP, incorporating structural and operational parameters, showcases significant improvements in predictive accuracy and robustness over traditional polynomial regression and ML models under conventional HPO methods. Furthermore, the application of this surrogate model in multi-objective optimization design for ECPs significantly reduces development time and computational costs, offering a streamlined and cost-effective solution for optimizing ECP performance. This study highlights potential future research directions, including the integration of other ML enhancements and the inclusion of more comprehensive feature parameters, to further improve the model's universality and applicability.

关键词： Optimization Computational modeling Machine learning algorithms Motors Machine learning Pumps Integrated circuit modeling Electric coolant pump machine learning multi-objective optimization sparrow search algorithm surrogate model

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Fixed-Time Control with an Improved sparrow search algorithm for Robotic Arm Performance Optimization

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10096页

作者： Zhang, Ruochen Choi, Hyeung-Sik Jung, Dongwook Cho, Hyunjoon Anh, Phan Huy Nam Vu, Mai The Natl Korea Maritime & Ocean Univ Dept Mech Engn Busan 49112 South Korea Sejong Univ Dept Artificial Intelligence & Robot Seoul 05006 South Korea

This paper presents an innovative approach that integrates a fixed-time control (FTC) algorithm with an improved sparrow search algorithm (ISSA) to enhance the trajectory tracking accuracy of a two-degree-of-freedom (two-DOF) robotic arm. The FTC algorithm, which incorporates barrier Lyapunov function (BLF) and adaptive neural network strategies, ensures rapid convergence, effective vibration suppression, and the robust handling of system uncertainties and input saturation. The ISSA, inspired by the foraging behavior of sparrows, improves search efficiency through dynamic weight adjustments and chaotic mapping, balancing global and local search capabilities. By optimizing control parameters, ISSA minimizes tracking errors. Simulation results demonstrate that the combined FTC and ISSA approach significantly reduces tracking errors and improves response speed compared to the use of FTC alone, underscoring its potential for achieving high-precision control in robotic arms and offering a promising direction for precise robotic control applications.

关键词： fixed-time control sparrow search algorithm Barrier Lyapunov input saturation robotic arm

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A multi-strategy-guided sparrow search algorithm to solve numerical optimization and predict the remaining useful life of li-ion batteries

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JOURNAL OF SUPERCOMPUTING 2024年第11期80卷 16254-16300页

作者： Xue, Jiankai Shen, Bo Pan, Anqi Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai Peoples R China

In this paper, a novel optimization method is proposed based on the sparrow search algorithm, namely, multi-strategy-guided sparrow search algorithm (MGSSA). It is well-known that the basic SSA has limitations such as the slow convergence speed and vulnerability to local optimality. Surrounding these two issues, some strategies are presented in the MGSSA. Firstly, the newly introduced ring topology search strategy not only maintains the diversity of the entire population but also enhances the exploration ability of the SSA. Secondly, the proposed leader-based search strategy can improve exploitation ability of the SSA to prevent falling into local optimum as much as possible. Moreover, the coordinated learning strategy is put forward to better balance between the exploration and exploitation abilities. Finally, the MGSSA is compared with seventeen advanced algorithms on two well-known benchmark suites (i.e., CEC-2017 and CEC-2020). Meanwhile, the MGSSA-based forecasting approach is applied to predict the remaining useful life for lithium-ion batteries. The statistical results indicate that the MGSSA is a high-performance optimizer, which can not only solve the defects of the original SSA, but also obtain satisfactory solutions in both complex numerical optimization and real-world application problems.

关键词： Coordinated learning strategy Leader-based search strategy Ring topology search strategy Remaining useful life sparrow search algorithm

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