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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Sasikala, M. Radhika, A. Rajasekar, S.S. Samanvita, N. Sr University School of Computer Science and Artificial Intelligence Warangal India Sona College of Technology Department of Information Technology Salem India Velammal College of Engineering and Technology Eee Madurai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9798350313413

The recommendation system is an effective tool used for filtering the huge number of informations and that is highly spread due to the exchanging habits of personalization trends, system users and appearing internet access. Although the current recommender systems are famous for proving the accurate recommendations and it can suffer from several limitations such as sparsity, scalability, cold-start, etc. Because of the various existing techniques, the choice of techniques is a difficult task while creating the applications based on recommender systems. Additionally, every strategy appears with its collection of advantages, limitations and features which increases much more questions, that must be identified. In this paper, the proposed sparrow search Optimization algorithm for recommender system using four various datasets named as MovieLens 100 k, MovieLens 1 million, Jester and Epinion. The performance of the proposed algorithm is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall with K-means clustering algorithms. The obtained result shows that this proposed methodology provides high recommendation systems. © 2023 IEEE.

关键词： Clustering Cold-start Collaborative filtering Recommender System sparrow search algorithm

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Denoising method for Fiber Optic Gyro measurement signal of face slab deflection of concrete face rockfill dam based on sparrow search algorithm and variational modal decomposition

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SENSORS AND ACTUATORS A-PHYSICAL 2021年 331卷 112913-112913页

作者： Xu, Lang Cai, Desuo Shen, Wei Su, Huaizhi Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China 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 Changijang River Sci Res Inst Wuhan 430010 Peoples R China

In order to suppress the noise in the measurement signal of face slab deflection of concrete face rockfill dam (CFRD) by fiber optic gyro (FOG), a denoising method based on sparrow search algorithm (SSA) and variational mode decomposition (VMD) method is proposed in this paper. Firstly, the SSA algorithm was used to optimize parameters k and of VMD, the fitness function of the algorithm was established based on the value of the kth intrinsic mode function (IMF) and the center frequency of the k-1th IMF obtained by the VMD decomposition. Then, this paper proposed a method to extract useful IMFs by calculating the mean Mahalanobis distance (MMD) of the Fast Fourier transform (FFT) functions between each IMF and the residual component. Finally, through the noise reduction analysis of FOG dynamic rotation experimental signal and the measured signal in practical engineering application, and compared with the results of traditional empirical mode decomposition (EMD) and VMD methods, the superiority of the proposed method was verified. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Fiber optic gyro The deflection measurement of face slab Variational mode decomposition sparrow search algorithm Mean mahalanobis distance Signal denoising

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Improved sparrow search algorithm based DV-Hop Localization in WSN

Improved Sparrow Search Algorithm based DV-Hop Localization ...

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Chinese Automation Congress (CAC)

作者： Lei, Yin De, Gu Fei, Liu Jiangnan Univ Minist Educ Inst Automat Key Lab Adv Control Light Ind Proc Inst Automat Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781728176871

DV-Hop is a widely used positioning technology in WSN. Owing to the problem of low positioning accuracy, based on DV-Hop wireless sensor network positioning technology, an improved sparrow search algorithm (SSA) is presented. The improvement of DV-Hop mainly has two aspects. First, the double communication radius method is adopted to modify the minimum hop count between nodes to reduce the estimated distance error;Secondly, this improved algorithm does not use the commonly used least squares method, but uses SSA to estimate nodes' position;At the same time, SSA applies Levy flight strategy to further improve the performance. Finally, the positioning accuracy of the method in this paper is tested by simulation, and the results prove the superiority of this algorithm.

关键词： WSN DV-Hop double communication radius sparrow search algorithm Levy flight

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Improved sparrow search algorithm for inversion of geometric parameters of earthquake source faults

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Geodesy and Geodynamics 2025年

作者： Leyang Wang Xuekai Zhou Zhanglin Sun Can Xi Hao Xiao School of Surveying and Geoinformation Engineering East China University of Technology Nanchang 330013 China Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry and East China University of Technology Nanchang 330013 China

With the continuous improvement of the accuracy of geodetic deformation data, the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms. In this research, an improved sparrow search algorithm (SSA) is proposed for the seismic source parameter inversion problem. By replacing the original population generation in the improved algorithm with Latin hypercubic sampling, the sparrow search algorithm reduces the repetition of samples in the population initialization. Subsequently, the algorithm introduces adaptive weights in the discoverer generation phase of the sparrow algorithm and combines the Levy flight strategy to make the algorithm more comprehensive and improve the search accuracy during the whole iteration process. Therefore, the improved Latin hypercube-based sparrow search algorithm (ILHSSA) has better advantages in terms of iterative convergence speed and stability. In order to verify the performance of ILHSSA, the basic genetic algorithm (GA) and sparrow search algorithm (SSA) are examined and compared with ILHSSA by simulated earthquakes of two different earthquake types. The simulation experiments show that the improved algorithm ILHSSA outperforms SSA in accuracy and stability. Compared with the GA algorithm, ILHSSA can achieve the same inversion accuracy as GA, and it even surpasses GA in inversion speed and the inversion results of some parameters, demonstrating better stability. Finally, the improved algorithm is used for the 2017 Bodrum-Cos earthquake and the 2016 Amatrice earthquake in Italy. The inversion results all reflect the practicality and reliability of the improved algorithm.

关键词： sparrow search algorithm Latin hypercube Source parameter inversion Bodrum-Coase earthquake Amatrice earthquake

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Solar Radiation Prediction Based on the sparrow search algorithm, Convolutional Neural Networks, and Long Short-Term Memory Networks

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Processes 2025年第5期13卷 1308-1308页

作者： Shuai Du Jianxin Zou Xinli Zheng Ping Zhong College of Physics Donghua University Shanghai 201620 China

With the challenge of increasing global carbon emissions and climate change, the importance of solar energy as a clean energy source is becoming more pronounced. Accurate solar radiation prediction is crucial for planning and operating solar energy systems. However, the accurate measurement of solar radiation faces challenges due to the high cost of instruments, strict maintenance, and technical complexity. Therefore, this paper proposes a deep learning approach that integrates the sparrow search algorithm (SSA), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks for solar radiation forecasting. The study utilizes solar radiation data from Songjiang District, Shanghai, China, from 2019 to 2020 for empirical analysis. Initially, a correlation analysis was conducted to identify the main factors affecting the intensity of solar radiation, including temperature, clear-sky GHI, solar zenith angle, and relative humidity. Subsequently, the forecasting effectiveness of the model was compared on datasets of 10 min, 30 min, and 60 min, revealing that the model performed best on the 60 min dataset, with a determination coefficient (R2) of 0.96221, root mean square error (RMSE) of 65.9691, and mean absolute error (MAE) of 37.9306. Moreover, comparative experimental results show that the SSA-CNN-LSTM model outperforms traditional LSTM, BiLSTM, and CNN-LSTM models in forecasting accuracy, confirming the effectiveness of SSA in parameter optimization. Thus, the SSA-CNN-LSTM model provides a new and efficient tool for solar radiation forecasting, which is of significant importance for the design and management of solar energy systems.

关键词： solar radiation forecasting sparrow search algorithm convolutional neural network long short-term memory network parameter optimization

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A classification and prediction model with the sparrow search-probabilistic neural network algorithm for transformer fault diagnosis

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2024年第4期44卷 249-257页

作者： Hu, Ling Yin, Lanlan Mo, Feng Liang, Zhixun Ruan, Zhong Wang, Yuting Liuzhou Railway Vocat Tech Coll Artificial Intelligence Liuzhou 545616 Guangxi Peoples R China Hechi Univ Big Data & Comp Sci Hechi 546300 Guangxi Peoples R China Hechi Univ Math & Phys Hechi 546300 Guangxi Peoples R China

We present a fault prediction model for transformers to improve the accuracy of transformer fault (TF) prediction. The model is predicated on a probabilistic neural network (PNN) that is optimised using three gas ratios with the help of the sparrow search algorithm (SSA). First, we monitor real-time gas concentrations and calculate the essential gas ratio by installing a smart gas sensor inside the transformer. The PNN is optimised by using the SSA algorithm. Subsequently, a mapping model between gas ratios and fault types is established. At last, we assess the model's prediction performance by calculating the mean square error. The results we got demonstrate that this method achieves a prediction accuracy of 90%, which is superior to the back propagation (BP) network, the k-nearest neighbour (KNN), and the support vector machine (SVM). This research offers an efficient and dependable approach for TF prediction.

关键词： probabilistic neural network PNN transformer fault sparrow search algorithm SSA transformer fault diagnosis classification prediction

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A survey on sparrow search algorithms and their applications

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2024年第4期55卷 814-832页

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

The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based algorithm that has made some significant advances since its introduction in 2020. A detailed overview of the basic SSA and several SSA-based variants is presented in this paper. To be specific, first, the principle of the basic SSA is introduced including its search mechanism and implementation process. Second, many improved SSAs are reviewed including the hybrid, chaotic, adaptive, binary and multi-objective SSAs. In addition, the applications of the SSAs are presented in some real scenarios such as the machine learning areas, energy systems, path planning and image processing. Finally, further research directions of the SSAs are discussed. This survey paper aims to provide a timely review on the latest developments of the SSAs.

关键词： Engineering applications easy implementation population initialisation swarm intelligence sparrow search algorithm

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Learning sparrow algorithm With Non-Uniform search for Global Optimization

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE REsearch 2023年第1期14卷 1-31页

作者： Chen, Yifu Li, Jun Zhang, Lin Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China Jiangxi Univ Appl Sci Sch Artificial Intelligence Nanchang Jiangxi Peoples R China Jiangxi Univ Sci & Technol Sch Software Engn Nanchang Jiangxi Peoples R China

sparrow algorithm as a New Swarm Intelligence search algorithm, the sparrow algorithm has good optimization ability, but in complex environments, it still has certain limitations, such as weak learning ability. Therefore, this paper proposes a learning sparrow search algorithm for non-uniform search(sparrow search algorithm with non-uniform search, NSSSA). A learning behavior selection strategy is proposed, and saltation learning and a random walk learning are introduced respectively. To a certain extent, the algorithm avoided alling into the local optimum, and a non-uniform variable spiral search is proposed to balance the development and search capabilities of the algorithm. In the experimental simulation, the effectiveness of the NSSSA algorithm is verified by using the benchmark function, and it is tested on the CEC 2013 test set. Compared with the algorithms with better performance in recent years, the results show that the NSSSA algorithm has better universality. Finally, the NSSSA algorithm is applied to the WSN coverage optimization problem. The results show that NSSSA achieves more than 90% and 96% coverage on the two models of 50x50 and 100x100, respectively, which verifies the practicability of the algorithm.

关键词： CEC 2013 Non-Uniform Spiral search Random Walk Learning Saltation Learning sparrow search algorithm WSN

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A Repair Method for Missing Traffic Data Based on FCM, Optimized by the Twice Grid Optimization and sparrow search algorithms

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SENSORS 2022年第11期22卷 4304-4304页

作者： Li, Pengcheng Dong, Baotian Li, Sixian Chu, Rusi Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Henan Commun Planning & Design Inst Co Ltd Zhengzhou 450046 Peoples R China

Complete traffic sensor data is a significant prerequisite for analyzing the changing rules of traffic flow and formulating traffic control strategies. Nevertheless, the missing traffic data are common in practice. In this study, an improved Fuzzy C-Means algorithm is proposed to repair missing traffic data, and three different repair modes are established according to the correlation of time, space, and attribute value of traffic flow. First, a Twice Grid Optimization (TGO) algorithm is proposed to provide a reliable initial clustering center for the FCM algorithm. Then the sparrow search algorithm (SSA) is used to optimize the fuzzy weighting index m and classification number k of the FCM algorithm. Finally, an experimental test of the traffic sensor data in Shunyi District, Beijing, is employed to verify the effectiveness of the TGO-SSA-FCM. Experimental results showed that the improved algorithm had a better performance than some traditional algorithms, and different data repair modes should be selected under different miss rate conditions.

关键词： missing sensor data fuzzy C-means data repair mode twice grid optimization algorithm sparrow search algorithm

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A sheep dynamic counting scheme based on the fusion between an improved-sparrow-search YOLOv5x-ECA model and few-shot deepsort algorithm

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期206卷

作者： Cao, Yuanyang Chen, Jian Zhang, Zichao China Agr Univ Coll Engn Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Trop South China Guangzhou 510642 Peoples R China Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518000 Peoples R China MNR Key Lab Spatial temporal Big Data Anal & Applicat Shanghai 200063 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Jiangsu Univ Jiangsu Prov & Educ Minist Cosponsored Synergist I Ctr Modern Agr Equipment Zhenjiang 212013 Peoples R China

In order to improve the accuracy of sheep counting and avoid the interference of mutual occlusion caused by different moving speed among sheep, the concept of fusion between the improved YOLOv5x model based on the attention mechanism and DeepSort algorithm is proposed. First, the ECA structure that is channel attention mechanism is used to optimize YOLOv5x model to strengthen the ability of capturing global information. Secondly, the sparrow search algorithm based on elite opposition-based learning strategy is used to optimize the learning rate of the detection model, so as to get the weight information of the optimal group to further improve the recognition rate of sheep. In the experiment, 800 high-resolution sheep images augmented by SRGAN network and data augmentation are used as model datasets, and the best weight information obtained by the YOLOv5x-ECA-SSA* model is used to accurately recognize sheep. According to the DeepSort algorithm, the recognized sheep are tracked, predicted and matched optimally. The experimental results show that the test precision of YOLOv5x*, YOLOv5x-ECA* and YOLOv5x-ECA-SSA* based on the SRGAN and data enhancement to train are respectively 95.74%, 96.50% and 97.10%. The error rate of each model combined with DeepSort algorithm to complete sheep dynamic counting is respectively 13%, 12% and 5%. Among them, the YOLOv5x-ECASSA* model has the highest mAP and best effect of sheep counting. The result can provide a new theorical method for realizing intelligent dynamic counting and tracking in the grazing process and provide a new technical application for intelligent animal husbandry.

关键词： YOLOv5x DeepSort algorithm sparrow search algorithm Attention Mechanism Sheep Dynamic Counting

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