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International Conference on Sensors and Instruments (ICSI)

作者： Jiang Zhen Hu Weiwei Qin huibin Hangzhou Dianzi Univ Coll Elect & Informat Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (数字)9781510646223

ISBN: (纸本)9781510646223

Aiming at the problem that DV-Hop algorithm leads to large positioning error when nodes are in irregular areas, a DV-Hop algorithm optimized by improved sparrow search algorithm (ISSA)is proposed. Firstly, the maximum hop distance error is used to correct the hop distance from the unknown node to each anchor node to reduce the estimated distance error. Secondly, Levy flight is introduced to enhance the ability of sparrow search algorithm to jump out of local optimum, and adopting Powell local search to enhanced convergence of the algorithm. Finally, the simulation results show that in irregular areas, compared with the classic DV-Hop algorithm, the positioning error of the improved algorithm is greatly reduced, and the positioning accuracy is effectively improved.

关键词： DV-Hop maximum hop distance error sparrow search algorithm Levy flight Powell local search

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Multi-Strategy Improvement of sparrow search algorithm for Cloud Manufacturing Service Composition

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Wuhan University Journal of Natural Sciences 2024年第4期29卷 323-337页

作者： ZHOU Liliang LI Ben YU Qing DAI Guilan ZHOU Guofu Tenth Research Institute China Electronics Technology Group CorporationChengdu 610000SichuanChina Department of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina Research Institution of Information Technology Tsinghua UniversityBeijing 100084China School of Computer Science Wuhan UniversityWuhan 430072HubeiChina

In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information *** this paper,we introduce an improved sparrow search algorithm(ISSA)to address these *** fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target *** enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation ***,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more ***,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization *** experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other *** results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition.

关键词： cloud manufacturing service composition optimization quality of service sparrow search algorithm

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Indoor Positioning Model Based on Support Vector Regression Optimized by the sparrow search algorithm 11

Indoor Positioning Model Based on Support Vector Regression ...

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Li, Hua Su, Jun Liu, Wufei Mang, Yucheng Zhou, Xianjing Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Wuhan Zall Informat Technol Co Ltd Wuhan 430000 Hubei Peoples R China

ISBN: (纸本)9781665426053

With the popularity and development of mobile smart terminals and wireless LANs, the demand for instant information data is increasing, thus indoor positioning is particularly significant. WiFi positioning technology is widely used for indoor positioning due to its low cost, easy implementation, wide coverage, and strong communication capability. Location fingerprint method is a location algorithm in WiFi positioning technology, which does not have high requirements for an indoor network environment. The positioning results of indoor positioning are susceptible to interference from external factors such as the positioning environment. In order to improve the accuracy and stability of localization, the population initialization process of the sparrow search algorithm (SSA) is optimized using logistic chaos mapping, and the support vector regression (SVR) machine is optimized with an improved sparrow search algorithm to obtain an indoor positioning prediction model. The model is compared and analyzed with GA-SVR and PSO-SVR through simulation experiments to show the superiority of the model.

关键词： sparrow search algorithm support vector regression indoor positioning

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Coordinated Dispatch of Combined Heat and Power Microgrid Based on the Improved sparrow search algorithm 11

Coordinated Dispatch of Combined Heat and Power Microgrid Ba...

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11th International Conference on Power and Energy Systems (ICPES)

作者： Xia, Lei Gao, Jin Liao, Qinglong Yue, Xingui Xiang, Hong Wu, Xiaodong Tan, Xiao State Grid Chongqing Elect Power Co Elect Power Res Inst Chongqing Peoples R China

ISBN: (纸本)9781665413251

In the context of the "dual-carbon" strategy, to improve the efficiency of the Micro-grid combined heat and power (MCHP), this paper proposed a multi-objective optimization scheduling model based on an improved sparrow search algorithm. First, with the goal of the lowest comprehensive operating cost and the lowest environmental pollutant discharge, the economic benefit objective function and the environmental benefit objective function are established respectively. Then, a dynamic adaptive weight is introduced instead of a single weighting algorithm, and the sparrow search algorithm is integrated to establish a multi-objective optimal scheduling model for comprehensive energy. Finally, taking a typical daily operation as an example, different optimization scenarios are set to verify the proposed scheduling optimization model. The result shows that the multi-objective setting can better meet the power grid dispatching requirements than the single-objective setting. The optimization algorithm proposed in this paper can further improve the economic and environmental benefits of the integrated energy system.

关键词： MCHP sparrow search algorithm optimal scheduling renewable energy

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Optimal Control of Chilled Water System Based on Improved sparrow search algorithm

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BUILDINGS 2022年第3期12卷 269页

作者： Zhu, Qixin Zhuang, Mengxiang Liu, Hongli Zhu, Yonghong Suzhou Univ Sci & Technol Sch Mech Engn Suzhou 215009 Peoples R China Suzhou Univ Sci & Technol Jiangsu Prov Key Lab Intelligent Bldg Energy Effi Suzhou 215009 Peoples R China Suzhou Univ Sci & Technol Sch Environm Sci & Engn Suzhou 215009 Peoples R China Jingdezhen Ceram Inst Sch Mech & Elect Engn Jingdezhen 333001 Peoples R China

Chilled water systems have large time delays and large inertia, and the traditional PID controller has a poor control effect. In this paper, an improved sparrow search algorithm is proposed to optimize the control of chilled water systems. Firstly, the random walk strategy was used to randomly perturb the sparrows to improve the searching ability of the sparrows. Then, a Gauss mutation was added in the iteration process of sparrows to enhance the local search ability. Finally, the values of the PID parameters as obtained by the above methods were substituted into the controller for simulation. The simulation results show that the method proposed in this paper improves the search accuracy of the sparrow search algorithm and effectively solves the problems of large time delays and large inertia in the chilled water system. The method in this paper took the least amount of time for the system to reach the steady state at only 12.75 s. The control effect of the proposed method was also better than that of the improved ant colony optimization algorithm. The rise time was 2.713 s, and the adjustment time was 4.95 s.

关键词： sparrow search algorithm random walk strategy Gauss mutation PID parameter optimization chilled water system

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A vector-encirclement-model-based sparrow search algorithm for engineering optimization and numerical optimization problems

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Hong, Jiale Shen, Bo Xue, Jiankai Pan, Anqi Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China

sparrow search algorithm (SSA) is a new global optimization tool with high performance. In order to further improve its global and local search abilities, in this paper, an improved SSA (ISSA) is put forward where the main novelties lie in the producer centralization strategy, the vector encirclement model and the direction selection strategy. The producer centralization strategy is designed to update the position of producer with the hope of improving the global search ability of the producer, while the vector encirclement model and the direction selection strategy are proposed to update the position of scrounger for the purpose of improving the local search ability of scrounger. For the comparison purpose, the performances of the proposed ISSA and the existing excellent algorithms are tested in CEC2017 benchmark functions and 30 real-world constrained optimization problems. The experimental results demonstrate that the proposed ISSA substantially improves the convergence rate, optimization accuracy, stability of the SSA and can solve a wide range of real-world constrained optimization problems successfully with satisfactory performances. Finally, the proposed ISSA is successfully applied in the trajectory optimization of mechanical arms.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Engineering optimization problems Numerical optimization problems sparrow search algorithm Trajectory optimization Vector encirclement model

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Prediction of Resistance Spot Welding Quality Based on BPNN Optimized by Improved sparrow search algorithm

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MATERIALS 2022年第20期15卷 7323-7323页

作者： Hu, Jianming Bi, Jing Liu, Hanwei Li, Yang Ao, Sansan Luo, Zhen Tianjin Univ Sch Mat Sci & Engn Tianjin 300354 Peoples R China Tianjin Long March Launch Vehicle Mfg Co Ltd Tianjin 300462 Peoples R China

Accurately predicting resistance spot welding (RSW) quality is essential for the manufacturing process. In this study, the RSW process signals of 2219/5A06 aluminum alloy under two assembly conditions (including gap and spacing) were analyzed, and then artificial intelligence modeling was carried out. To improve the performance and efficiency of RSW quality evaluation, this study proposed a multi-signal fusion method that was performed by combining principal component analysis and a correlation analysis. A backpropagation neural network (BPNN) model was optimized using the sine-chaotic-map-improved sparrow search algorithm (SSA), and the input and output of the model were the variables after multi-signal fusion and the button diameter, respectively. Compared with the standard BPNN model, the Sine-SSA-BP model reduced the MAE by 42.33%, MSE by 51.84%, and RMSE by 31.45%. Its R-2 coefficient reached 0.6482, which is much higher than that of BP (0.2464). According to various indicators (MAE, MSE, RMSE, and R-2), the evaluation performance of the Sine-SSA-BP model was better than that of the standard BPNN model. Compared with other models (BP, GA-BP, PSO-BP, SSA-BP, and Sine-PSO-BP), the evaluation performance of the Sine-SSA-BP model was best, which can successfully predict abnormal spot welds.

关键词： resistance spot welding quality prediction sparrow search algorithm sine chaotic mapping backpropagation neural network

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Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems

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INFORMATION PROCESSING & MANAGEMENT 2022年第2期59卷 102854-102854页

作者： Ma, Jie Hao, Zhiyuan Sun, Wenjing Jilin Univ Sch Management Changchun 130022 Peoples R China Jilin Univ Res Ctr Big Data Management Changchun 130022 Peoples R China Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun 130117 Peoples R China

As a recent swarm intelligence optimization algorithm, sparrow search algorithm (SSA) is widely adopted in many real-world problems. However, the solutions to the limitations of SSA (such as low accuracy of convergence and tendency of trapping into local optimum) are still not available. To address these issues, we propose an enhanced multi-strategies sparrow search algorithm (EMSSA) based on three strategies specifically addressing the limitations of SSA: 1) in the uniformity-diversification orientation strategy, we propose an adaptive-tent chaos theory to allow more diversity and greater randomness in the initial population;2) in the hazard-aware transfer strategy, we construct a weighted sine and cosine algorithm based on the growth function to avoid trapping into the state of local optima stagnation;3) in the dynamic evolutionary strategy, we design the similar perturbation function and introduce the triangle similarity theory to improve the exploration capability. The performance of EMSSA in solving the continuous optimization problems about the 23 benchmark functions, CEC2014, and CEC2017 problems is much improved than that of SSA and other state-of-the-art algorithms. Furthermore, the results of the density peak clustering optimization show that the EMSSA outperforms SSA.

关键词： sparrow search algorithm Uniformity-diversification orientation strategy Hazard-aware transfer strategy Dynamic evolutionary strategy Density peak clustering

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A novel oil pipeline leakage detection method based on the sparrow search algorithm and CNN

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MEASUREMENT 2022年 204卷

作者： Li, Qi Shi, Yaru Lin, Ruiqi Qiao, Wenxu Ba, Wei Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116023 Peoples R China Dalian Univ Technol Key Lab Intelligent Control Optimizat Ind Equipment Minist Educ Dalian 116023 Peoples R China Dalian Jiaotong Univ Coll Elect & Informat Engn Dalian 116023 Peoples R China

A novel sparrow search algorithm and convolutional neural network (SSA-CNN) method is proposed for oil pipeline leakage detection. Firstly, the proposed SSA-CNN method converts the input data from time series to two-dimensional matrix, and the classification conditions of different convolution kernel sizes and different pooling sizes are compared. Then, the SSA algorithm is used to optimize the parameters of the CNN. The simulation results show that, compared with the traditional machine learning method, using two-dimensional data as input can enhance the neural network's extraction of eigenvalues. The proposed SSA-CNN method was able to accurately classify 148 sample points in 150 test sets with an accuracy rate of 98.67%, which is not only higher than traditional machine learning methods, but also further improves the classification capability of CNN, while the SSA-CNN method can use the parameters already learned in the test set to ensure real-time detection.

关键词： Oil pipeline leakage Deep learning algorithm Convolutional neural network Fault diagnosis sparrow search algorithm

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RBF neural network modeling and application based on improved sparrow search algorithm

RBF neural network modeling and application based on improve...

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第35届中国控制与决策会议

作者： Jian Song Qiumei Cong Xingqiu Jiang Mengyuan Zhang Jian Yang Shuaishuai Yang Liaoning Petrochemical University School of Information and Control Engineering

Using sparrow search algorithm(SSA) to optimize neural network model is easy to fall into local extremum, which leads to low accuracy of soft *** solve this problem,this paper proposes an improved sparrow search algorithm CSSA) to optimize the widths,centers and weights of radial basis function neural network(RBFNN).Taking the mean square error value as the individual fitness value,the position update formulas of discoverers,participants and early warning persons in SSA are improved to optimize the parameters of ***,the convergence of the algorithm was proved,and the chemical oxygen demand(COD) was predicted using the random data validation and the measured data of the sewage treatment *** results showed that the mean absolute percentage error of SSA-RBF neural network was 1.55%,the mean absolute percentage error of ISSA-RBF neural network was 1.26%,and the mean absolute percentage error decreased by 0.29%,It shows that ISSA-RBF neural network has high soft-sensing accuracy.

关键词： sewage treatment RBF neural network sparrow search algorithm soft sensor

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