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Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

SCIENTIFIC REPORTS 2024年第1期14卷 1-32页

作者： Samantaray, Sandeep Sahoo, Abinash Satapathy, Deba Prakash Oudah, Atheer Y. Yaseen, Zaher Mundher Natl Inst Technol Srinagar Dept Civil Engn Hazratbal 190006 Jammu & Kashmir India Odisha Univ Technol & Res Dept Civil Engn Bhubaneswar Odisha India Univ Thi Qar Coll Educ Pure Sci Dept Comp Sci Nasiriyah 64001 Iraq Al Ayen Univ Sci Res Ctr Informat & Commun Technol Res Grp Nasiriyah Iraq King Fahd Univ Petr & Minerals Civil & Environm Engn Dept Dhahran 31261 Saudi Arabia

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash-Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

关键词： sparrow search algorithm Suspended sediment load Support vector machine Brahmani river

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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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Optimal chiller loading by improved sparrow search algorithm for saving energy consumption

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JOURNAL OF BUILDING ENGINEERING 2023年 67卷

作者： Xue, Zhilu Yu, Junqi Zhao, Anjun Zong, Yue Yang, Siyuan Wang, Meng Xian Univ Architecture & Technol Coll Informat & Control Engn Xian 710000 Peoples R China Xian Univ Architecture & Technol Coll Construct Equipment Sci & Engn Xian 710000 Peoples R China

Aims to reduce the energy consumption in a heating, ventilation, and air conditioning (HVAC) system with improper load distribution. An efficient optimization method, the improved sparrow search algorithm, has been developed to address the optimal chiller loading (OCL) problem for parallel chillers system. In general, the optimization goal is to minimize the system's energy consumption subject to meeting load demands, and the partial load rate of each chiller is taken as the optimization variable. This algorithm utilizes the Circle chaotic mapping to initialize the position to improve the quality and diversity of the initial solution. Meanwhile, to improve the algorithm's optimization accuracy, the information exchange reinforcement mechanism of the Gray Wolf Optimizer is used to update the producer position. Besides, the chaotic sine cosine mechanism is combined with the scrounger position update to enhance the convergence speed of the algorithm. Eventually, three case studies are selected to evaluate the performance of ISSA in detail and compared with other optimization algorithms. Experimental simulation verified that the improved sparrow search algorithm is more energy-saving and has the advantages of fast convergence, short running time and good robustness.

关键词： Optimal chiller loading Parallel chillers system sparrow search algorithm Energy saving

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Sine-SSA-BP Ship Trajectory Prediction Based on Chaotic Mapping Improved sparrow search algorithm

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SENSORS 2023年第2期23卷 704-704页

作者： Zheng, Yuanzhou Li, Lei Qian, Long Cheng, Bosheng Hou, Wenbo Zhuang, Yuan Wuhan Univ Technol Sch Nav Wuhan 430036 Peoples R China Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Wuhan 430036 Peoples R China

Objective: In this paper, we propose a Sine chaos mapping-based improved sparrow search algorithm (SSA) to optimize the BP neural network for trajectory prediction of inland river vessels because of the problems of poor accuracy and easy trapping in local optimum in BP neural networks. Method: First, a standard BP model is constructed based on the AIS data of ships in the Yangtze River section. A Sine-BP model is built using Sine chaos mapping to assign neural network weights and thresholds. Finally, a Sine-SSA-BP model is built using the sparrow search algorithm (SSA) to solve the optimal solutions of the neural network weights and thresholds. Result: The Sine-SSA-BP model effectively improves the initialized population of uniform distribution, and reduces the problem that population intelligence algorithms tend to be premature. Conclusions: The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability than conventional LSTM and SVM, especially in the prediction of corners, which is in good agreement with the real ship navigation trajectory.

关键词： ship trajectory prediction sparrow search algorithm sine chaos mapping BP neural network

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Research on camera calibration optimization method based on improved sparrow search algorithm

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JOURNAL OF ELECTRONIC IMAGING 2023年第1期32卷 013040-013040页

作者： Guo, Jia Zhu, Yun Wang, Jianyu Du, Shuai He, Xin Nanjing Univ Sci & Technol Sch Automation Nanjing Peoples R China

Accurate calibration of camera internal parameters is an important step in realizing three-dimensional object reconstruction and target location. Large reprojection error is a frontier problem in the field of camera calibration. A real calibration image dataset and a reprojection error model are established. In combination with the fast iteration of elite opposition-based learning strategy and the population diversity of sparrow search algorithm, the calibration parameters were optimized to improve the accuracy of calibration parameters and reduce the reprojection error. Experimental results demonstrate the effectiveness of the proposed algorithm. It can effectively reduce the reprojection error.

关键词： Camera calibration sparrow search algorithm elite opposition-based learning

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MSSSA:a multi-strategy enhanced sparrow search algorithm for global optimization

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1828-1847页

作者： Kai MENG Chen CHEN Bin XIN School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Compler Systems Beijing 100081China

The sparrow search algorithm(SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal optimization problems. Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced sparrow search algorithm(MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an adaptive parameter control strategy is designed to accommodate an adequate balance between exploration and exploitation. Finally, a hybrid disturbance mechanism is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering optimization problems. The results demonstrate the superiority of the MSSSA in addressing practical problems.

关键词： Swarm intelligence sparrow search algorithm Adaptive parameter control strategy Hybrid disturbance mechanism Optimization problems

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A Multi-Strategy Improved sparrow search algorithm for Coverage Optimization in a WSN

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SENSORS 2023年第8期23卷 4124-4124页

作者： Chen, Hui Wang, Xu Ge, Bin Zhang, Tian Zhu, Zihang Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 232001 Peoples R China

To address the problems of low monitoring area coverage rate and the long moving distance of nodes in the process of coverage optimization in wireless sensor networks (WSNs), a multi-strategy improved sparrow search algorithm for coverage optimization in a WSN (IM-DTSSA) is proposed. Firstly, Delaunay triangulation is used to locate the uncovered areas in the network and optimize the initial population of the IM-DTSSA algorithm, which can improve the convergence speed and search accuracy of the algorithm. Secondly, the quality and quantity of the explorer population in the sparrow search algorithm are optimized by the non-dominated sorting algorithm, which can improve the global search capability of the algorithm. Finally, a two-sample learning strategy is used to improve the follower position update formula and to improve the ability of the algorithm to jump out of the local optimum. Simulation results show that the coverage rate of the IM-DTSSA algorithm is increased by 6.74%, 5.04% and 3.42% compared to the three other algorithms. The average moving distance of nodes is reduced by 7.93 m, 3.97 m, and 3.09 m, respectively. The results mean that the IM-DTSSA algorithm can effectively balance the coverage rate of the target area and the moving distance of nodes.

关键词： wireless sensor network coverage optimization sparrow search algorithm non-dominated sorting two-sample learning strategy

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Application of Improved sparrow search algorithm in Electric Battery Swapping Station Switching Dispatching

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023年第3期16卷 1页

作者： Shi, Qingsheng Zhao, Feifan Henan Univ Technol Coll Mech & Elect Engn Zhengzhou Peoples R China Henan Univ Technol Coll Elect Engn Zhengzhou Peoples R China

The demand for power exchange has risen dramatically as the number of electric vehicle (EV) users has increased, and large-scale disorderly charging will increase the operating costs of battery swapping stations and increase the risk to the power grid. Through experimental comparison with other algorithms on 23 test functions, the results demonstrate that the convergence accuracy and speed of this improved algorithm are superior to those of other algorithms. Furthermore, in solving the optimization problem of EV battery swapping station scheduling, by reasonably allocating the battery pack charging time and establishing the forecasting model of switching demand, the average variance and peak-to-average ratio of the grid are reduced by 29.55% and 13.2%, respectively, based on meeting the user's switching demand. Approximately a 12,500 RMB reduction in the cost of charging lowers the operation cost and risk of battery swapping stations and enhances the user experience.

关键词： algorithm improvement battery swapping station Electric vehicle firefly interference sparrow search algorithm

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Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

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INTERNET OF THINGS 2023年 22卷

作者： Khaleel, Mustafa Ibrahim Univ Sulaimani Kurdistan Reg Govt Coll Sci Comp Dept Sulaimani 46001 Iraq

The job scheduling paradigms include dispatching Internet of Things (IoT) critical services onto processing nodes. Here most energy is consumed in finding suitable virtual machines (VMs) that can execute IoT tasks without resource fragments. Therefore, a significant problem is minimizing energy consumption through efficient task placement that leads to load balance and minimizes resource leakage. To resolve this problem, we proposed a dual-phase metaheuristic algorithm called CSSA-DE. First, we conduct a clustering approach to group computing nodes into effective clusters. Each node is trained at different utilization levels, and the one that can yield the highest Performance-to-Power Ratio (PPR) is selected as the mega cluster head (MCH). Then, we integrated the sparrow search algorithm (SSA) with the differential evolution (DE) algorithm to expand the high search efficiency of finding an appropriate pair task-VM combination. Further, the integration phase can exploit the count of overloaded and underloaded VMs, reducing resource fragments. The performance of CSSA-DE is highly competitive and relatively better in multiple cases compared to state-of-the-art algorithms.

关键词： Cloud data center Virtual machine Energy consumption sparrow search algorithm Differential evolution algorithm

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Optimization of urban emergency support material distribution under major public health emergencies based on improved sparrow search algorithm

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SCIENCE PROGRESS 2023年第2期106卷 368504231175328页

作者： Pan, Nan Ye, Xiao Cao, Jianing Zhang, Jingcheng Han, Yuhang He, Zhaolei Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut 727 Jingming South Rd Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming Peoples R China Yunnan Power Grid Co Ltd Measurement Ctr Kunming Peoples R China

The outbreak of major public health emergencies such as the coronavirus epidemic has put forward new requirements for urban emergency management procedures. Accuracy and effective distribution model of emergency support materials, as an effective tool to inhibit the deterioration of the public health sector, have gradually become a research hotspot. The distribution of urban emergency support devices, under the secondary supply chain structure of "material transfer center-demand point," which may involve confusing demands, is studied to determine the actual situation of fuzzy requests under the impact of an epidemic outbreak. An optimization model of urban emergency support material distribution, based on Credibility theory, is first constructed. Then an improved sparrow search algorithm, ISSA, was designed by introducing Sobol sequence, Cauchy variation and bird swarm algorithm into the structure of the classical SSA. In addition, numerical validation and standard test set validation were carried out and the experimental results showed that the introduced improved strategy effectively improved the global search capability of the algorithm. Furthermore, simulation experiments are conducted, based on Shanghai, and the comparison with existing cutting-edge algorithms shows that the designed algorithm has stronger superiority and robustness. And the simulation results show that the designed algorithm can reduce vehicle cost by 4.83%, time cost by 13.80%, etc. compared to other algorithms. Finally, the impact of preference value on the distribution of emergency support materials is analyzed to help decision-makers to develop reasonable and effective distribution strategies according to the impact of major public health emergencies. The results of the study provide a practical reference for the solution of urban emergency support materials distribution problems.

关键词： Emergency support material distribution vehicle routing problem fuzzy demand Credibility theory sparrow search algorithm

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