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LOAD FORECASTING OF sparrow search algorithm OPTIMIZATION DOUBLE BIGRU

COMPUTING AND INFORMATICS 2024年第3期43卷 561-582页

作者： Wu, Di Yang, Lijun Ma, Wenli Hebei Univ Engn Dept Informat & Elect Engn Handan Hebei Peoples R China

In this paper, a PCA-SSA-DBIGRU-Attention multi-factor short-term power load forecasting model is proposed. Taking a complete account of the influence of meteorological factors, principal components analysis (PCA) is used to analyze the meteorological factors of daily minimum, maximum, daily average temperature, relative humidity, daily precipitation and power load data at the same time. The realization of original load data is dimensioned down. The complexity of power load forecasting models is reduced. Then, the Attention Double Bidirectional Gating Recurrent Unit (DBIGRU) model is constructed to calculate the different weights of the hidden layer states of the two-layer BIGRU. The hidden layer states are assigned different weights. The sparrow search algorithm (SSA) is incorporated into the DBIGRU-Attention. The SSA-DBIGRU-Attention network model is constructed to optimize the learning rate, the number of iterations and the four hyperparameters of the first and second hidden layer neurons. The extracted principal components are input into SSA-DBIGRU-Attention to realize multi-factor short-term power load forecasting. Experimental results show that the prediction accuracy of the proposed model is improved, and the prediction time is reduced. Compared to the VMD-BILSTM, PCA-DBILSTM, CNN-GRU-Attention and CNN-BIGRU-Attention model, the four aspects of MAPE, MAE, RMSE and time are reduced by 29.55 %, 36.42 %, 32.34% and 12.22 %, respectively, the R 2 is improved by 3.09 %.

关键词： Power load forecasting principal component analysis sparrow search algorithm two-layer BIGRU attention mechanism

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Short-term power load forecasting based on sparrow search algorithm-variational mode decomposition and attention-long short-term memory

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1089-1097页

作者： Duan, Qinwei He, Xiangzhen Chao, Zhu Tang, Xuchen Li, Zugang Power Dispatch Guangdong Power Grid Control Ctr 75 Meihua Rd Guangzhou 510000 Guangdong Peoples R China Beijing Qingneng Internet Technol Co LTD 1 Longshi St Beijing 100084 Peoples R China

To improve the forecasting accuracy of power load, the forecasting model based on sparrow search algorithm (SSA), variational mode decomposition (VMD), attention mechanism and long short-term memory (LSTM) was proposed. Firstly, SSA is used to optimize the number of decomposition and penalty factor in VMD and realize the decomposition operation of the initial data. Then, LSTM is used to predict each component, and on this basis, feature and temporal attention mechanisms are introduced. Feature attention mechanism is introduced to calculate the contribution rate of relevant input features in real time, and the feature weights are modified to avoid the limitations of traditional methods relying on the threshold of expert experience association rules. Temporal attention mechanism is applied to extract the historical key moments and improve the stability of the time series prediction effect. Finally, the final result is obtained by superimposing the prediction results of each component to complete the power load prediction. Practical examples show that, compared with other methods, the proposed model achieves the highest prediction accuracy, with an RMSE of 1.23, MAE of 0.99 and MAPE of 11.62%.

关键词： load forecasting sparrow search algorithm variational mode decomposition attention mechanism long short-term memory

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Multi-Objective Optimization of Steel Pipe Pile Cofferdam Construction Based on Improved sparrow search algorithm

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10407页

作者： Jiang, Zaolong Yang, Chengfang Yue, Hongbo Hunan Univ Sch Civil Engn Changsha 410082 Peoples R China Hunan Huda Construct Supervis Co Ltd Changsha 410082 Peoples R China China Railway Seventh Bur Grp Third Engn Co Ltd Xian 710032 Peoples R China

This paper develops a multi-objective optimization model to address the absence of systematic and practical evaluation methods for selecting construction schemes for steel pipe pile cofferdams. The model aims to minimize duration and cost while maximizing quality. Additionally, it proposes an improved sparrow search algorithm (ISSA) to solve this problem. First, a tent chaotic map is introduced to initialize the sparrow population, enhancing the diversity of the initial population. Second, the principle of non-dominated ordering is introduced to sort the parent and offspring populations during the iteration process, and the appropriate individuals are selected to form the offspring population. Finally, gray correlation analysis is applied to optimize the Pareto solution set and determine the final construction scheme. The effectiveness and superiority of the ISSA is verified by using the Changsha Jinan Avenue project as a case study. The results indicate that the quality of the optimized construction scheme remains at a high level of 0.90 or more;the duration is shortened by 18 days, a reduction of 21%;and the total cost is reduced by CNY 220,000, saving 3% of the cost.

关键词： sparrow search algorithm multi-objective optimization steel pipe pile cofferdam non-dominated sorting tent chaotic map

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The Modified sparrow search algorithm with Brown Motion and Levy Flight Strategy for the Class Integration Test Order Generation Problem

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BIOMIMETICS 2025年第4期10卷 195-195页

作者： Jiao, Chongyang Zhou, Qinglei Zhang, Wenning Zhang, Chunyan Informat Engn Univ Lab Adv Comp & Intelligence Engn Zhengzhou 450001 Peoples R China Zhengzhou Vocat Coll Ind Safety Henan Informat Engn Sch Zhengzhou 450000 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Software Coll Zhengzhou 450007 Peoples R China

Software testing identifies potential errors and defects in software. A crucial component of software testing is integration testing, and the generation of class integration test orders (CITOs) is a critical topic in integration testing. The research shows that search-based algorithms can solve this problem effectively. As a novel search-based algorithm, the sparrow search algorithm (SSA) is good at finding the optimal to optimization problems, but it has drawbacks like weak population variety later on and the tendency to easily fall into the local optimum. To overcome its shortcomings, a modified sparrow search algorithm (MSSA) is developed and applied to the CITO generation issue. The algorithm is initialized with a good point set strategy, which distributes the sparrows evenly in the solution space. Then, the discoverer learning strategy of Brownian motion is introduced and the Levy flight is utilized to renew the positions of the followers, which balances the global search and local search of the algorithm. Finally, the optimal solution is subjected to random wandering to increase the probability of the algorithm jumping out of the local optimum. Using the overall stubbing complexity as a fitness function to evaluate different class test sequences, experiments are conducted on open-source Java systems, and the experimental results demonstrate that the MSSA generates test orders with lower stubbing cost in a shorter time than other novel intelligent algorithms. The superiority of the proposed algorithm is verified by five evaluation indexes: the overall stubbing complexity, attribute complexity, method complexity, convergence speed, and running time. The MSSA has shown significant advantages over the BSSA in all aspects. Among the nine systems, the total overall stubbing complexity of the MSSA is 13.776% lower than that of the BSSA. Total time is reduced by 23.814 s.

关键词： integration testing test order sparrow search algorithm stubbing complexity Brownian motion Levy flight

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Workshop layout optimization method based on sparrow search algorithm: a new approach

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2024年第4期41卷 324-343页

作者： Bi, Shenghao Shao, Liangshan Zheng, Jiatian Yang, Rui Liaoning Tech Univ Sch Business Adm Huludao Peoples R China Liaoning Inst Sci & Engn Jinzhou Peoples R China

Reasonable workshop layout can reduce the transportation distance, reduce the production cost and improve the production efficiency. Aiming at the problem of unreasonable workshop layout, this paper takes an actual case as the research object and studies its workshop layout. Firstly, the mathematical model with the objectives of minimizing logistics transportation cost and maximizing non-logistics relationship is established. Then, the sparrow search algorithm (SSA) is used to find the optimal layout that satisfies the objective function. Finally, Flexsim software is used to simulate the layout scheme. The results show that compared with the genetic algorithm and the original layout scheme, the layout scheme obtained by the SSA algorithm reduces the total transportation distance by 26 m and 325 m, and improves the non-logistic relationship by 1.6% and 24.84%, respectively. Optimizing the layout using SSA algorithms increases product productivity, which is a good improvement over the original workshop layout.

关键词： Workshop layout optimization mathematical model sparrow search algorithm

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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Karim, Faten Khalid Varela-Aldas, Jose Ishak, Mohamad Khairi Aljarbouh, Ayman Mostafa, Samih M. Princess Nourah Bint Abdulrahman Univ PNU Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh 11671 Saudi Arabia Univ Tecnol Indoamer Fac Ingn Ctr Invest Ciencias Humanas & Educ CICHE Ingn Ind Ambato 180103 Ecuador Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn Ajman U Arab Emirates Univ Cent Asia Sch Arts & Sci Dept Comp Sci Naryn Kyrgyzstan South Valley Univ Fac Comp & Informat Dept Comp Sci Qena Egypt New Assiut Technol Univ NATU Fac Ind & Energy Technol New Asyut 71515 Egypt

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the sparrow search algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform.

关键词： Industrial internet of things Cyberattack detection Bayesian machine learning Chameleon optimization algorithm sparrow search algorithm

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Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control for delta robot based on parameter optimization of sparrow search algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第12期239卷 4726-4743页

作者： Liu, Jian Guo, Zhongfeng Yang, Junlin Shenyang Univ Technol 111 Shenliao West RdEcon & Technol Dev Zone Shenyang 110870 Peoples R China

Aiming at the characteristics of nonlinearity, strong coupling and unknown perturbation in the dynamics model of Delta robot under high-speed operation, this paper designs a new Global Non-singular Fast Terminal Sliding Mode Control (GNFTSMC), which avoids the singularity of the traditional terminal sliding mode control. On this basis, Fuzzy Global Non-singular Fast Terminal Sliding Mode Control (FGNFTSMC) is proposed by combining Global Non-singular Fast Terminal Sliding Mode Control with fuzzy system, which is a method of eliminating the chattering of sliding mode controller by using fuzzy logic outputs instead of the reaching control law. To improve the trajectory tracking accuracy, the Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control (AFGNFTSMC) is proposed by adjusting output gain online through the adaptive fuzzy system. The sparrow search algorithm (SSA) is proposed to optimize the Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control (AFGNFTSMC) sliding surface parameters and select the optimal sliding surface parameters. Experiments show that applying the Adaptive Fuzzy Global Non-singular Fast Terminal Sliding Mode Control optimized based on the SSA (SSA-AFGNFTSMC) to the Delta robot improves the tracking accuracy, reduces the convergence time and eliminates the controller output chattering.

关键词： Delta robots Global Non-singular Fast Terminal Sliding Mode adaptive fuzzy system sparrow search algorithm chattering reduction

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Application of uniform experimental design theory to multi-strategy improved sparrow search algorithm for UAV path planning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Cheng, Lianyu Ling, Guang Liu, Feng Ge, Ming-Feng Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China

The sparrow search algorithm (SSA) is a meta-heuristic optimization algorithm based on the predatory behavior of sparrows. However, SSA tends to fall into the local optimum when solving optimization problems with complex constraints. To improve its optimization efficiency and overall performance, this paper develops a multi-strategy improved SSA (ISSA) based on uniform experimental design theory. Specifically, the wraparound L2-discrepancy (WD), as a uniformity metric for uniform design of experiments, is fully utilized with macro-regulation, adaptive dynamic management strategy, and boundary redistribution management mechanism to quantify the population uniformity in each iteration of ISSA. Inspired by the concept of uniform design, WD is initially adopted to gauge population uniformity, and the threshold acceptance (TA) algorithm is employed to produce an initial population with improved uniformity, hence augmenting the population's diversity and quickening the rate of convergence. Secondly, a macroscopical individual iterative strategy of the producer is adjusted to avoid the population converging to the origin. Then, a dynamic population uniformity affiliation function based on WD is introduced to adjust the population uniformity affiliation function according to the relative amount of change in the global optimum, and the number of danger perceivers is adjusted according to the affiliation function. What is more, a new boundary update strategy is also proposed in ISSA based on population uniformity. By comparing ISSA on 23 standard test functions and recently updated validation function set CEC2022 with the original SSA, and some classical as well as newly developed algorithms, the superiority of the present ISSA is thoroughly confirmed. As an application case, the ISSA algorithm is utilized to solve the path planning problem of the complex environment of unmanned aerial vehicle (UAV) based on threat models by applying it to 2D maps containing circular and poly

关键词： sparrow search algorithm Uniform design Dynamic affiliation function UAV path planning Wrap-around L2-discrepancy

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Multi-strategy improved sparrow search algorithm based on first definition of ellipse and group co-evolutionary mechanism for engineering optimization problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 14005-14035页

作者： Chen, Gang Sun, Hu Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

The sparrow search algorithm (SSA) is recognized for its rapid convergence and precision in engineering optimization, yet it faces the challenge of premature convergence on complex problems. To address this, a multi-strategy improved sparrow search algorithm (MISSA) is proposed to enhance the optimization performance and applicability in this study. For the first time in the algorithm, the first definition of ellipses is integrated into SSA to balance its exploration and exploitation capabilities. A group co-evolutionary mechanism is introduced to promote population diversity and suppress premature convergence. Unlike most existing work, ablation experiments are utilized to evaluate the effective impact of these enhancement strategies on SSA. Statistical results based on the Wilcoxon signed-rank test and Friedman test show that the dynamic regulator based on the first definition of ellipses has the greatest impact on improving the performance of SSA. Numerical experiments based on the CEC2017 benchmark problems are used as an optimization case to compare MISSA with the classical metaheuristic algorithm and other state-of-the-art variants of SSA. The results demonstrate the outstanding performance and immense potential of MISSA in problem-solving. The applicability of the proposed algorithm is validated through six actual engineering optimization problems, showcasing strong competitiveness in global optimization.

关键词： sparrow search algorithm Engineering optimization First definition of ellipse Group co-evolutionary mechanism Global optimization

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Adaptive Feature Extraction Using sparrow search algorithm-Variational Mode Decomposition for Low-Speed Bearing Fault Diagnosis

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SENSORS 2024年第21期24卷 6801页

作者： Wang, Bing Tang, Haihong Zu, Xiaojia Chen, Peng Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan 316022 Peoples R China Mie Univ Grad Sch Tus Mie 5148507 Japan Mie Univ Fac Bioresources Tus Mie 5148507 Japan

To address the challenge of extracting effective fault features at low speeds, where fault information is weak and heavily influenced by environmental noise, a parameter-adaptive variational mode decomposition (VMD) method is proposed. This method aims to overcome the limitations of traditional VMD, which relies on manually set parameters. The sparrow search algorithm is used to calculate the fitness function based on mean envelope entropy, enabling the adaptive determination of the number of mode decompositions and the penalty factor in VMD. Afterward, the optimised parameters are used to enhance traditional VMD, enabling the decomposition of the raw signal to obtain intrinsic mode function components. The kurtosis criterion is then used to select relevant intrinsic mode functions for signal reconstruction. Finally, envelope analysis is applied to the reconstructed signal, and the results reveal the relationship between fault characteristic frequencies and their harmonics. The experimental results demonstrate that compared with other advanced methods, the proposed approach effectively reduces noise interference and extracts fault features for diagnosing low-speed bearing faults.

关键词： fault diagnosis VMD sparrow search algorithm kurtosis criterion signal analysis low-speed bearing

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