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Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models

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Journal of Mountain Science 2023年第10期20卷 2852-2868页

作者： SHUI Kuan HOU Ke-peng HOU Wen-wen SUN Jun-long SUN Hua-fen Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China Yunnan Key Laboratory of Sino-German Blue Mining and Utilization of Special Underground Space Kunming 650093China College of Water Resource and Hydropower Sichuan UniversityChengdu 610065China

The safety factor is a crucial quantitative index for evaluating slope ***,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their ***,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety *** this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample *** Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction *** sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction *** mean square error(MSE)of the predicted and true values and the fitting of the data are compared and *** MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data *** study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional ***,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction *** model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate

关键词： Multi-layer regression algorithm fusion Stacking gensemblelearning sparrow search algorithm Slope safety factor Data prediction

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A Multi-strategy Improved sparrow search algorithm and its Application

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NEURAL PROCESSING LETTERS 2023年第9期55卷 12309-12346页

作者： Yang, Yongkuan Xu, Jianlong Kong, Xiangsong Su, Jun Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & I Xiamen 361024 Peoples R China

In order to address the issues of slow convergence and susceptibility to falling into the local optimum trap of the original sparrow search algorithm, a novel multi-strategy improved sparrow search algorithm (MSSSA) is proposed. Firstly, an improved tent chaotic mapping is introduced to enhance the diversity and quality of the initial population distribution. Secondly, an adaptive adjustment strategy of population division is incorporated to balance the global search and local exploitation capabilities of algorithm. Furthermore, To improve the convergence performance, the sinusoidal function is applied to update the explorer and vigilant. Finally, an adaptive perturbation strategy is proposed to assist the algorithm in escaping local optimal solutions. To evaluate the effectiveness of the proposed improved strategy, 13 classical test functions and the CEC2017 test suite were selected to validate the performance of MSSSA. the Friedman test and Wilcoxon test results also verify the significance of the results, the effectiveness and convergence of the improved strategy. In addition, the improved algorithm was applied to predict the medium-term electricity load of the microgrid, and the parameters of the gated recurrent unit neural network were optimally predicted on two actual electricity load datasets. The experimental comparison further confirms the effectiveness and feasibility of the proposed improved algorithm in practical applications.

关键词： sparrow search algorithm Tent chaotic mapping Adaptive perturbation strategy Medium-term power load forecasting

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Multi-label feature selection based on HSIC and sparrow search algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第8期20卷 14201-14221页

作者： Wang, Tinghua Zhou, Huiying Liu, Hanming Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Peoples R China

Feature selection has always been an important topic in machine learning and data mining. In multi-label learning tasks, each sample in the dataset is associated with multiple labels, and labels are usually related to each other. At the same time, multi-label learning has the problem of "curse of dimensionality". Feature selection therefore becomes a difficult task. To solve this problem, this paper proposes a multi-label feature selection method based on the Hilbert-Schmidt independence criterion (HSIC) and sparrow search algorithm (SSA). It uses SSA for feature search and HSIC as feature selection criterion to describe the dependence between features and all labels, so as to select the optimal feature subset. Experimental results demonstrate the effectiveness of the proposed method.

关键词： Keyword feature selection multi-label classification sparrow search algorithm Hilbert-Schmidt independence criterion (HSIC) data mining

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Application of an improved sparrow search algorithm in BP network classification of strip steel surface defect images

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第10期82卷 14403-14439页

作者： Ma, Guoyuan Yue, Xiaofeng Gao, Xueliang Liu, Fuqiuxuan Changchun Univ Technol Changchun Jilin Peoples R China

In this paper, the BP neural network optimized by an improved sparrow search algorithm is proposed (WLT-SSA-BP), which performs excellently in the classification task of strip steel defect images. In the current work, an adaptive weight is introduced into the WLT-SSA algorithm, and the weight is affected by the number of population iterations and the size of the fitness value, which enhances the global search ability of the algorithm;A Levy flight strategy is introduced into WLT-SSA, which helps the algorithm jump out of the local optimal solution through a short-distance local search and occasional longer-distance walking;At the same time, the population t distribution strategy is introduced into the population initialization, which effectively prevents the aggregation of the population. Finally, the proposed WLT-SSA and BP neural network are modeled. The experimental results show that the accuracy rate of WLT-SSA-BP in classifying defect images can reach 96.62%, which is comparable to some other typical heuristic algorithm-optimized BP networks. Its accuracy, precision, recall, specificity, and F1 score were increased by 0.5%-4.66%, 2.62%-8.1%, 1.71%-7.77%, 0.72%-1.33%, 1.59%-8.73%, respectively.

关键词： Image classification BP neural network sparrow search algorithm Adaptive weight Levy flight T distribution

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Identification of feedback nonlinear systems with time delay based on chaotic decreasing weight sparrow search algorithm

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SOFT COMPUTING 2024年第5期28卷 4009-4024页

作者： Li, Junhong Yan, Jun Zhang, Hongrui Xiao, Kang Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This paper considers the identification of feedback nonlinear systems with unknown time delay (FNTD) by the chaotic decreasing weight sparrow search algorithm (CWSSA). The CWSSA algorithm uses the improved circle chaos to map the sparrow population, which avoids the problems of clustering and low coverage in the solution space of the initial solution. Thus, the global search ability and convergence speed of the algorithm are improved. By introducing a linear decreasing weight, the risk of the sparrow search algorithm being prone to premature maturity is reduced, and the oscillation phenomenon that is easy to occur near the global optimal solution in the later stage of the algorithm is avoided. Using the search capability of CWSSA and the iterative identification technology, all parameters and the unknown delay of the FNTD system are estimated simultaneously. Finally, the identification results of CWSSA, SSA and PSO are compared through a numerical example. The results show that CWSSA is superior to SSA and PSO in terms of convergence speed and estimation accuracy. The effectiveness of the CWSSA is also verified by the identification of the electro-hydraulic servo position system.

关键词： Feedback nonlinear system Parameter estimation Time delay sparrow search algorithm Chaotic decreasing weight

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sparrow search algorithm with adaptive t distribution for multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期238卷

作者： Zhang, Huizhen Huang, Qin Ma, Liang Zhang, Ziying Univ Shanghai Sci & Technol Sch Management Mail Box 459516 Jungong Rd Shanghai 200093 Peoples R China Shanghai Univ Engn Sci Sch Mat Engn 333 Longteng Rd Shanghai 201620 Peoples R China

In this paper, we present a multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time (MOLCMTPP-FDFT) that minimizes both cost and time while incorporating mandatory carbon emission, carbon tax, carbon trading, and carbon offset policies. Chance constrained programming and interval programming are introduced to formulate the fuzzy demand chance constrained programming model and the fuzzy time interval programming model, respectively. Then, the mathematical model of uncertain MOLCMTPP-FDFT is transformed into a deterministic model. Based on the sparrow search algorithm, t distribution and the concept of Pareto optimality for multi-objective optimization, we also propose a solution strategy for the proposed model. In this algorithm, the number of iterations is used as the degree of freedom of -to update the sparrow location, which strikes a balance between the capabilities of global search and local search. Finally, the proposed algorithm and MOLCMTPP-FDFT are applied to a real case, resulting in a minimum cost of 260730.48 and a time duration of 13.044, which outperform the minimum cost of 268874.88 and minimum time of 18.32 obtained using single-mode transportation. The carbon emissions resulting from the lowest-cost solution obtained using single-mode transportation are 3,198.48, which are significantly more than the allowed emissions. Therefore, the proposed algorithm and mathematical model of MOLCMTPP-FDFT are valuable tools for optimizing multimodal transportation route. Additionally, the experimental results not only validate the superior efficiency and energy-saving benefits of the proposed multimodal transportation routes in comparison to the actual single-modal transportation, but also demonstrate the applicability of different low-carbon policies.

关键词： Low-carbon multimodal transportation sparrow search algorithm Adaptive t distribution Chance constrained programming Interval programming

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Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals

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AUTOMATIKA 2025年第2期66卷 321-334页

作者： Ling, Liuyi Lin, Zhu Feng, Bin Wei, Liyu Jin, Li Wang, Yiwen Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Artificial Intelligence Huainan Peoples R China Anhui Univ Sci & Technol Inst Environm Friendly Mat & Occupat Hlth Wuhu Peoples R China

Continuous prediction of human joint angles is crucial for enhancing the performance of man-machine cooperative control. This study proposed a random forest (RF) model optimized by the sparrow search algorithm (SSA) for achieving continuous motion prediction of the lower limb knee joint based on surface electromyography(sEMG) under various motion modes. The sEMG signal and knee angle were recorded during four human motions, namely normal gait, sitting-standing transition, ascending stairs and descending stairs. These signals were processed to remove noise and extract eigenvalues in both time and frequency domains. A knee angle prediction model based on SSA-RF algorithm was trained and evaluated using feature sample data. In the four motion mode experiments, the SSA-RF model achieved a minimum root-mean-square error of 1.569 degrees for predicting knee joint angle, the average absolute error was only 1.05 degrees, and the coefficient of determination was as high as 0.99. The performance of the proposed model was compared with those of traditional backpropagation neural network, support vector machine regression, and random forest models. The comparison results clearly indicate that the proposed model can more effectively predict knee joint angles under various motion modes and performs better in promoting human-machine cooperation.

关键词： Surface electromyography signal continuous motion prediction sparrow search algorithm random forest knee joint

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MSXFGP: combining improved sparrow search algorithm with XGBoost for enhanced genomic prediction

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BMC BIOINFORMATICS 2023年第1期24卷 1-21页

作者： Zhou, Ganghui Gao, Jing Zuo, Dongshi Li, Jin Li, Rui Inner Mongolia Agr Univ Coll Comp & Informat Engn Erdos East St 29 Hohhot 010011 Peoples R China Inner Mongolia Autonomous Reg Key Lab Big Data Re Zhaowuda Rd 306 Hohhot 010018 Peoples R China Inner Mongolia Autonomous Reg Big Data Ctr Chilechuan St 1 Hohhot 010091 Peoples R China

Background: With the significant reduction in the cost of high-throughput sequencing technology, genomic selection technology has been rapidly developed in the field of plant breeding. Although numerous genomic selection methods have been proposed by researchers, the existing genomic selection methods still face the problem of poor prediction accuracy in practical applications. Results: This paper proposes a genome prediction method MSXFGP based on a multi-strategy improved sparrow search algorithm (SSA) to optimize XGBoost parameters and feature selection. Firstly, logistic chaos mapping, elite learning, adaptive parameter adjustment, Levy flight, and an early stop strategy are incorporated into the SSA. This integration serves to enhance the global and local search capabilities of the algorithm, thereby improving its convergence accuracy and stability. Subsequently, the improved SSA is utilized to concurrently optimize XGBoost parameters and feature selection, leading to the establishment of a new genomic selection method, MSXFGP. Utilizing both the coefficient of determination R-2 and the Pearson correlation coefficient as evaluation metrics, MSXFGP was evaluated against six existing genomic selection models across six datasets. The findings reveal that MSXFGP prediction accuracy is comparable or better than existing widely used genomic selection methods, and it exhibits better accuracy when R-2 is utilized as an assessment metric. Additionally, this research provides a user-friendly Python utility designed to aid breeders in the effective application of this innovative method. MSXFGP is accessible at https://***/ DIBreeding/MSXFGP. Conclusions: The experimental results show that the prediction accuracy of MSXFGP is comparable or better than existing genome selection methods, providing a new approach for plant genome selection.

关键词： Genome selection sparrow search algorithm XGBoost Parameter optimization Feature selection

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Study on load monitoring and demand side management strategy based on Elman neural network optimized by sparrow search algorithm

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CIRCUIT WORLD 2023年第1期49卷 56-66页

作者： Fan, Yuanyuan Sui, Tingyu Peng, Kang Sang, Yingjun Huang, Fei Huaiyin Inst Technol Meicheng Rd Campus Huaian Peoples R China

Purpose This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal. Design/methodology/approach The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved. Findings The paper analyzes the energy saving effect of energy efficiency management as well as "avoiding peak and filling valley" measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM. Research limitations/implications Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further. Practical implications The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user. Originality/value This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.

关键词： Elman neural network Demand side management Load monitoring sparrow search algorithm

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Review and empirical analysis of sparrow search algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第10期56卷 10867-10919页

作者： Yue, Yinggao Cao, Li Lu, Dongwan Hu, Zhongyi Xu, Minghai Wang, Shuxin Li, Bo Ding, Haihua Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Intelligent Informat Syst Inst Wenzhou 325035 Peoples R China Wenzhou Univ Technol Wenzhou Key Lab Intelligent Lifeline Protect & Eme Wenzhou 325035 Peoples R China

In recent years, swarm intelligence algorithms have received extensive attention and research. Swarm intelligence algorithms are a biological heuristic method, which is widely used in solving optimization problems. The traditional swarm intelligence algorithms provide new ideas and new ways to solve some practical problems, and they have made positive progress in fields such as combinatorial optimization, task scheduling, process control, engineering prediction, and image processing. In particular, the sparrow search algorithm is a new type of group intelligence optimization algorithm inspired by the group foraging behavior to perform local and global search by imitating the foraging and anti-predation behavior of sparrows. In view of the shortcomings of the original sparrow search algorithm, such as its easy fall into local optimum, slow convergence speed, and low convergence accuracy, scholars at home and abroad have improved the sparrow search algorithm and have made practical applications in various fields. Firstly, this paper introduces the basic principle of sparrow search algorithm, analyzes the factors affecting the performance of the algorithm, further proposes the improvement strategy of the algorithm, and performs function test comparison and performance analysis with particle swarm optimization algorithm, monarch butterfly algorithm, colony spider algorithm, and pigeon swarm optimization algorithm. After that, the application and development of the sparrow search algorithm in power grid load forecasting, image processing, path tracking, wireless sensor network routing performance optimization, wireless location, and fault diagnosis are described. Finally, combined with the performance characteristics and application direction of the sparrow search algorithm, the future research and development direction of the sparrow search algorithm is prospected.

关键词： Swarm intelligence Evolutionary computation Bio-inspired algorithm sparrow search algorithm Improvement strategy

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