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Multi-strategy sparrow search algorithm with non-uniform mutation

SYSTEMS SCIENCE & CONTROL ENGINEERING 2022年第1期10卷 936-954页

作者： Huang, Zuwei Zhu, Dongli Liu, Yujia Wang, Xiao Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China Jiangxi Coll Applicat Sci & Technol Sch Artificial Intelligence Nanchang Jiangxi Peoples R China Zhejiang Normal Univ Xingzhi Coll Jinhua Zhejiang Peoples R China

sparrow search algorithm (SSA) suffers from a tendency to fall into local optima, as well as a preference for zero locations. Therefore, to improve this drawback, we propose a non-uniform mutation sparrow search algorithm (NMSSA). In the initialization stage of the population, we introduce a tent chaos map and a generalized opposition-based learning strategy to improve the diversity of the population;We introduce adaptive weight to dynamically adjust the search range of the discoverer to improve the search efficiency of the algorithm;To prevent the algorithm from deviating from the target in the early stage, we adopt a non-uniform mutation strategy to improve the flexibility of the follower search to improve the convergence accuracy of the algorithm. Finally, we use the somersault strategy to reduce the probability of the algorithm falling into local optimum. In the test experiments with 10 benchmark functions and CEC2017 functions, we compare the experimental results of NMSSA with those of other algorithms, and the experimental results verify the effectiveness of NMSSA. In addition, we also applied NMSSA to engineering problems optimization and K-means image segmentation, and the experimental results show that NMSSA has good performance in practical applications.

关键词： sparrow search algorithm tent map generalized opposition-based learning adaptive weight non-uniform mutation somersault engineering problems K-means image segmentation

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An Integrated Method Based on sparrow search algorithm Improved Variational Mode Decomposition and Support Vector Machine for Fault Diagnosis of Rolling Bearing

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2022年第8期10卷 2893-2904页

作者： Wang, Mengjiao Wang, Wenjie Zeng, Jinfang Zhang, Yibing Xiangtan Univ Sch Automat & Elect Informat North Second Ring Rd Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Sch Phys & Optoelect North Second Ring Rd Xiangtan 411105 Hunan Peoples R China

Purpose The main purpose of this paper is to reduce the influence of penalty parameter (alpha) and mode number K on VMD decomposition. Furthermore, the optimized VMD is combined with support vector machine (SVM) to diagnose rolling bearing faults. Methods This paper proposed a parameter adaptive VMD method based on the sparrow search algorithm (SSA), which is named SSA-VMD. First, the minimum mean envelope entropy (MEE) as the objective function to find the best parameters combination. Then, VMD with the optimized parameters is used to decompose the signals and obtain the corresponding components. In order to extract fault features better, the fuzzy entropy (FE) of intrinsic mode functions (IMFs) which are attained by SSA-VMD as feature vectors. Finally, input the feature vectors into the support vector machine which is combined with the SSA to improve accuracy, so as to get the classification result. Results Two public data sets are used to verify the effectiveness of SSA-VMD-SVM. The experimental results show that this method is faster than other methods in decomposition speed and has better performance in fault classification of rolling bearings. Conclusion In this paper, a method for adaptive selection of VMD parameters is proposed, and combined with SVM, a method for fault diagnosis of rolling bearings is formed. At the same time, the method can distinguish single fault and mixed fault well, and has a good classification effect. This result helps to provide a new fault diagnosis method.

关键词： Variational mode decomposition sparrow search algorithm Fuzzy entropy Support vector machine Rolling bearing

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A Novel sparrow search algorithm for Microgrid Optimal Scheduling with Electric Vehicles 38

A Novel Sparrow Search Algorithm for Microgrid Optimal Sched...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Ying, Ludi Shen, Bo Xue, Jiankai Pan, Anqi Donghua University School of Information Science and Technology Shanghai China

ISBN: (纸本)9798350303636

In this paper, a new improved sparrow search algorithm (ISSA) based on the swarm-intelligence technique method is proposed for the optimal scheduling problem of the microgrid (MG) model with electric vehicles (EVs). In order to make a better reasonable scheduling plan for the power generation plan of each power source on the MG, the ISSA is used to optimize the power distribution. Two strategies are used to improve the basic SSA. One is replace the producer update formula with the fractional particle swarm position update formula, and it can enhance the searching ability of the ISSA. The other one is multi-producer following strategy, which can better balance the global exploration and local exploitation of the ISSA. The effectiveness of the ISSA is verified via some well-known benchmark functions. The experimental results demonstrate that the ISSA performs better compared to other algorithms. Finally, the ISSA is successfully applied to a MG model based on two EV charge-discharge control strategies. © 2023 IEEE.

关键词： electric vehicle Microgrid optimal scheduling sparrow search algorithm swarm intelligence

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AUV Path Planning Based on Improved sparrow search algorithm 23

AUV Path Planning Based on Improved Sparrow Search Algorithm

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Wang, Siyuan Sun, Qian Ning, Beixi Zhou, Xingyu College of Information and Communication Harbin Engineering University Key Laboratory of Advanced Marine Communication and Information Technology Harbin China

ISBN: (纸本)9798350325959

An improved sparrow search algorithm (ISSA) is used to study the three-dimensional path-planning problem of automatic underwater vehicle (AUV). Use mathematical models to build the environment and construct an energy consumption model based on the AUV and environmental conditions. Firstly, based on the upper confidence bound algorithm (UCB), the iterative population size is improved to utilize search resources at different times fully;Secondly, considering the undirected search of the discoverer, an adaptive greedy algorithm is adopted to enhance convergence accuracy;Finally, improve the evolutionary strategy of discoverers and threatened sparrows to reduce the algorithm falling into local optima. The simulation results show that ISSA has better global optimization capability, making it more suitable for AUV 3D path planning. © 2023 IEEE.

关键词： Adaptive greedy algorithm Automatic underwater vehicle sparrow search algorithm Upper confidence bound

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Prediction of Coiled Tubing Erosion Rate Based on sparrow search algorithm Back-Propagation Neural Network Model

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9519-9519页

作者： Cao, Yinping Fang, Fengying Wang, Guowei Zhu, Wenyu Hu, Yijie Xian Shiyou Univ Mech Engn Coll Xian 710065 Peoples R China

Coiled tubing has been widely used in oilfield development because it can significantly improve oil well productivity and recovery efficiency. However, with the increase in fracturing, drilling, and sand-washing operations, the erosion of coiled tubing walls caused by solid particles has become one of the main failure modes. To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. The results show that the main influencing factors for coiled tubing erosion rate are impact velocity, impact angle, and sand concentration. In addition, the SSA-BPNN model shows a high goodness of fit (R) and a good fit with the experimental data. The SSA-BPNN model underwent standard statistical validation tests, effectively predicting the erosion rate of coiled tubing with a high coefficient of determination and low errors, demonstrating a robust consistency between predicted and actual values. This study is of great significance to oilfield engineers, pipeline designers, and oilfield developers, and provides effective support for optimizing oilfield development and pipeline maintenance. The main users include oil companies, engineering consulting institutions and related industry personnel, and may also attract the interest of scientific research institutions and academia, providing a useful reference for the technological progress of the oil industry.

关键词： coiled tubing sparrow search algorithm back-propagation neural network erosion rate prediction grey relational analysis

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The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm and attention mechanism

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ENERGY 2024年 288卷

作者： Cui, Xiwen Yu, Xiaoyu Niu, Dongxiao North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China North China Elect Power Univ Sch Econ & Management 2 Beinong Rd Beijing 102206 Peoples R China

Ensuring the efficient scheduling of power systems and enhancing the grid's renewable energy integration efficiency heavily relies on the precision and dependability of wind power prediction. Based on this, an ultra-shortterm wind power point-interval prediction framework is proposed. Firstly, the wind power data is preprocessed. Secondly, Random Forest(RF) is used to filter the factors affecting wind power data to eliminate redundant features. Third, using improved sparrow search algorithm(ISSA) to ascertain the optimal parameters in variational mode decomposition(VMD), the wind power sequence is decomposed to obtain a more regular sequence. Then, combining ISSA, bidirectional gated recurrent unit(BiGRU) and Attention, the ISSA-BiGRU-Attention model is constructed for the prediction of wind power subsequences. Finally, using the kernel density estimation(KDE) of Grid search(GS)-Cross-Validation(CV), prediction intervals for wind power at varying confidence levels are calculated. The experimental results show that the RF-ISSA-VMD-ISSA-BiGRU-Attention prediction model has the great prediction accuracy compared with the comparison models. In dataset 1, the model possesses a good prediction result with an R2 of 0.997408 which is an improvement of 7.98 % compared with LSTM. In addition, the GS-CV-KDE interval prediction model enhances the practicability of prediction outcomes and offers more efficient prediction information for the assurance of power system safety and reliability.

关键词： Wind power prediction Deep learning prediction Attention mechanism sparrow search algorithm

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Enhanced sparrow search algorithm based on improved game predatory mechanism and its application

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DIGITAL SIGNAL PROCESSING 2024年 145卷

作者： Yang, Jiahui Gao, Shesheng Zhao, Xuehua Li, Guo Gao, Zhaohui Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Xian Shiyou Univ Sch Elect Engn Xian 710065 Peoples R China

In response to the problems of strong randomness in initialization, imperfect search mechanism and tendency to fall into local optimum when dealing with complex optimization problems by sparrow search algorithm (SSA), an enhanced sparrow search algorithm (ESSA) is proposed, which combines Hammersley low difference sequence (HLDS) and centroid opposition-based learning (COBL), improved game predatory mechanism (GPM) and elastic collision strategy (EC). The ESSA can improve the initial individual quality, and avoid the attraction of local optima. In addition, the ESSA can effectively improve the search mechanism by increasing information exchange of individuals and balancing global search and local development. The effectiveness of ESSA is verified through simulation experiments of benchmark functions and variational modal decomposition (VMD) parameter optimization.

关键词： sparrow search algorithm Game predatory mechanism Elastic collision strategy VMD parameter optimization

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Research on the optimal capacity configuration of green storage microgrid based on the improved sparrow search algorithm

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FRONTIERS IN ENERGY REsearch 2024年 12卷

作者： Zhu, Nan Ma, Xiaoning Guo, Ziyao Shen, Chen Liu, Jie State Grid Liaoning Elect Power Supply Co Ltd Branch Mat Shenyang Peoples R China State Grid Corp China Beijing Peoples R China Shenyang Univ Technol Sch Elect Engn Shenyang Peoples R China

Green storage plays a key role in modern logistics and is committed to minimizing the environmental impact. To promote the transformation of traditional storage to green storage, research on the capacity allocation of wind-solar-storage microgrids for green storage is proposed. Firstly, this paper proposes a microgrid capacity configuration model, and secondly takes the shortest payback period as the objective function, and uses the improved sparrow search algorithm (ISSA) for optimization. Firstly, the Logistic-Tent compound chaotic mapping method is added to the population initialization of the sparrow search algorithm (SSA). Secondly, the adaptive t-distribution mutation is used to improve the discoverer, and the overall optimization ability of the algorithm is improved. Finally, the hybrid decreasing strategy is adopted in the process of vigilance position update. The ISSA can improve the search efficiency of the algorithm, avoid premature convergence and enhance the robustness of the algorithm, which is helpful to better apply to the optimal configuration of wind-solar-storage microgrid capacity in green storage. By analyzing the optimal capacity allocation results of two typical days, the system can better adapt to the dynamic storage requirements and improve the flexibility and sustainability of the supply chain.

关键词： green storage microgrid capacity configuration wind-solar-storage system sparrow search algorithm

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Hand-Eye Calibration of Surgical Robots Based on a BP Neural Network Optimized by Using an Improved sparrow search algorithm 17

Hand-Eye Calibration of Surgical Robots Based on a BP Neural...

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17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Zhu, Jiaqi Ning, Weibo Yuan, Ye Chen, Hongjiang Zhou, Weijun Tan, Yecheng He, Shuxing Hu, Jun Fan, Zhun Shantou Univ Shantou Peoples R China Shantou Univ Med Coll Affiliated Hosp 1 Shantou Peoples R China

ISBN: (纸本)9781665476874

Hand-eye calibration methods for surgical robots are employed to derive a transformation between the robot's base motor and visual coordinate systems. Accurately completing hand-eye calibration procedures provides an important guarantee that a surgical robot will exhibit positioning and execution accuracy sufficient for assisting surgeons in successfully completing surgical procedures. To improve the accuracy of robot hand-eye calibration methods based on backpropagation neural network (BPNN) models, we propose a modified BP neural network optimized using the sparrow search algorithm for hand-eye calibration model (TSSABPNN), which can enhance population initialization by applying tent mapping. Furthermore, we also design a new sliding 3D calibration tool. The sparrow search algorithm exhibits good local exploration ability, and we introduce a tent map with ergodic characteristics to initialize the sparrow population information, which further improves the network's global search ability and convergence rate. Finally, we experimentally analyze four calibration models: TSSABP NN model, a BP NN model optimized using a genetic algorithm of simulated annealing (GASABPNN), an unoptimized BP NN model, and the traditional singular value decomposition method. The results indicate that the proposed TSSABP NN model exhibits the maximum calibration precision and best robustness and iteratively converges faster.

关键词： Hand-eye calibration Nonlinear coordinate convert Intelligent optimization sparrow search algorithm Neural Network

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Improved sparrow search algorithm Based on Multistrategy Collaborative Optimization Performance and Path Planning Applications

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PROCESSES 2024年第12期12卷 2775页

作者： Xu, Kunpeng Chen, Yue Zhang, Xuanshuo Ge, Yizheng Zhang, Xu Li, Longhai Guo, Ce Xuzhou Coll Ind Technol Coll Mech & Elect Engn Xuzhou 221140 Peoples R China Xuzhou Univ Technol Sch Mech & Elect Engn Xuzhou 221018 Peoples R China Nanjing Univ Aeronaut & Astronaut Inst Bioinspired Struct & Surface Engn 29 Yudao St Nanjing 210016 Peoples R China

To address the problems of limited population diversity and a tendency to converge prematurely to local optima in the original sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) based on multi-strategy collaborative optimization is proposed. ISSA employs three strategies to enhance performance: introducing one-dimensional composite chaotic mapping SPM to generate the initial sparrow population, thus enriching population diversity;introducing the dung beetle dancing search behavior strategy to strengthen the algorithm's ability to jump out of local optima;integrating the adaptive t-variation improvement strategy to balance global exploration and local exploitation capabilities. Through experiments with 23 benchmark test functions and comparison with algorithms such as PSO, GWO, WOA, and SSA, the advantages of ISSA in convergence speed and optimization accuracy are verified. In the application of robot path planning, compared with SSA, ISSA exhibits shorter path lengths, fewer turnings, and higher planning efficiency in both single-target point and multi-target point path planning. Especially in multi-target point path planning, as the obstacle rate increases, ISSA can more effectively find the shortest path. Its traversal order is different from that of SSA, making the planned path smoother and with fewer intersections. The results show that ISSA has significant superiority in both algorithm performance and path planning applications.

关键词： sparrow search algorithm one-dimensional composite chaotic mapping dung beetle dancing search behavior strategy adaptive t-distribution robotic path planning

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