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Journal of Iron and Steel Research(International) 2023年第12期30卷 2459-2470页

作者： Wei Guan Yong-mei Zhu Jun-jie Bao Jian Zhang School of Mechanical Engineering Jiangsu University of Science and TechnologyZhenjiang212003JiangsuChina

The buckling load of carbon fiber composite cylindrical shells(CF-CCSs)was predicted using a backpropagation neural network improved by the Sparrow search algorithm(SSA-BPNN).Firstly,two CF-CCSs,each with an inner diameter of 100 mm,were manufactured and tested.The buckling behavior of CF-CCSs was analyzed by finite element and experiment.Subsequently,the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed,and the dataset of the neural network was generated using the finite element method.On this basis,the SSA-BPNN model for predicting buckling load of CF-CCS was established.The results show that the maximum and average errors of the SSA-BPNN to the test data are 6.88%and 2.24%,respectively.The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.

关键词： Composite cylindrical shell:Carbon fiber Backpropagation neural network Sparrow search algorithm Buckling

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

A bioinspired path planning approach for mobile robots based on improved Sparrow search algorithm

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Advances in Manufacturing 2022年第1期10卷 114-130页

作者： Zhen Zhang Rui He Kuo Yang School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444People’s Republic of China

In this paper,a bioinspired path planning approach for mobile robots is proposed.The approach is based on the Sparrow search algorithm,which is an intelligent optimization algorithm inspired by the group wisdom,foraging,and anti-predation behaviors of sparrows.To obtain high-quality paths and fast convergence,an improved Sparrow search algorithm is proposed with three new strategies.First,a linear path strategy is proposed,which can transform the polyline in the corner of the path into a smooth line,to enable the robot to reach the goal faster.Then,a new neighborhood search strategy is used to improve the fitness value of the global optimal individual,and a new position update function is used to speed up the convergence.Finally,a new multi-index comprehensive evaluation method is designed to evaluate these algorithms.Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-ofthe-art studies.

关键词： Path planning Linear path strategy Sparrow search algorithm Multi-index comprehensive evaluation algorithm

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 stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,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 factor.In 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 data.Random 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 accuracy.The 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 accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The 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 fitting.This study innovatively applies the Sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,m

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

A Chaos Sparrow search algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 331-364页

作者： Andi Tang Huan Zhou Tong Han Lei Xie Aeronautics Engineering College Air Force Engineering UniversityXi’an710038China

The Sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic Sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.

关键词： Sparrow search algorithm global optimization adaptive step benchmark function chaos map

Research on Evacuation Path Planning Based on Improved Sparrow search algorithm

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1295-1316页

作者： Xiaoge Wei Yuming Zhang Huaitao Song Hengjie Qin Guanjun Zhao The School of Building Environment Engineering Zhengzhou University of Light IndustryZhengzhou450001China Zhengzhou Key Laboratory of Electric Power Fire Safety Zhengzhou University of Light IndustryZhengzhou450001China Eccom Network System Shanghai201103China

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced Sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale algorithm,Gray Wolf algorithm,Improved Gray Wolf algorithm,Sparrow search algorithm,and MSSA algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the Sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

关键词： Sparrow search algorithm optimization and improvement function test set evacuation path planning

The SSA-BP-based potential threat prediction for aerial target considering commander emotion

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Defence Technology（防务技术） 2022年第11期18卷 2097-2106页

作者： Xun Wang Jin Liu Tao Hou Chao Pan College of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China Beijing Xin Li Machinery Limited Liability Company Beijing100039China School of Information and Communication Engineering Hubei University of EconomicsWuhan430205China

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system.However,the traditional threat prediction methods mostly ignore the effect of commander's emotion.They only predict a target's present threat from the target's features itself,which leads to their poor ability in a complex situation.To aerial targets,this paper proposes a method for its potential threat prediction considering commander emotion(PTP-CE)that uses the Bi-directional LSTM(BiLSTM)network and the backpropagation neural network(BP)optimized by the Sparrow search algorithm(SSA).Furthermore,we use the BiLSTM to predict the target's future state from real-time series data,and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model.Therefore,the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion.The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction,regardless of commander's emotional effect.

关键词： Aerial targets Emotional factors Potential threat prediction BiLSTM Sparrow search algorithm Neural network

Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database

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Underground Space 2023年第3期10卷 91-117页

作者： Yin Bo Xing Huang Yucong Pan Yanfang Feng Penghai Deng Feng Gao Ping Liu Quansheng Liu Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province School of Civil EngineeringWuhan UniversityWuhanHubei 430072China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhanHubei 430072China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanHubei 430071China

Accurately predicting and estimating the squeezing and ground response to tunneling remains challenging.Moreover,tunnel-squeezing hazards are much more likely to occur in deeply buried long tunnels with complex engineering-geological environments.There-fore,a high-performance predictive model for tunnel squeezing is necessary.A superior ensemble classifier is put forward in this study,which is composed of four individual classifiers(gradient boosting classifier,extra-trees classifier,AdaBoost classifier,and Logistic regression classifier)and two optimization algorithms(Bayesian optimization(BO)and Sparrow search algorithm(SSA)).The training database covers five parameters:tunnel depth(H),rock tunneling quality index(Q),tunnel diameter(D),support stiffness(K),and strength stress ratio(SSR),about which the basic information is accessible at the early design phases.However,the dataset compiled from the literature is insufficient.Thus,the ten proposed methods are used to replace the missing values.During the model training pro-cess,BO shows its strong ability to optimize seventeen hyperparameters.When applied to tune the classifiers’weights,SSA achieves a fast and efficient performance.The novel Shapley Additive Explanations–LightGBM method indicates that the K is the most important input feature,followed by SSR,Q,H,and D,respectively.The ensemble classifier is then validated using the test set and additional his-torical case projects.The validation shows that the model can achieve an accuracy of 98%(i.e.,the error rate is 2%)on the test set,higher than those achieved by previous prediction models.Moreover,the predicted probability could provide warning information for timely support measures.Finally,the application results are illustrated through tests on the tunnel sections that have not yet been excavated in the line of the Sichuan–Tibet railway project.The applied predictive tendencies and laws are in line with the practical experience.In sum-mary,the proposed model’s prediction results are reasonable,and its pre

关键词： Tunnel squeezing hazard Bayesian optimization Machine learning techniques Sparrow search algorithm Ensemble classifier Incomplete database

An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system

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Green Energy and Intelligent Transportation 2023年第2期2卷 56-65页

作者： Bingzhe Fu Wei Wang Yihuan Li Qiao Peng School of Control and Computer Engineering North China Electric Power UniversityBeijing102206China Group of Information Technology Analytics&OperationsQueen's University BelfastBelfastBT95EEUK

The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow search algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.

关键词： Battery management SOC estimation Data science Neural network Golden section method Sparrow search algorithm

Linear active disturbance rejection control of heavy-haul train operation based on an interval type-2 fuzzy logic system model

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Transportation Safety and Environment 2022年第4期4卷 130-139页

作者： Yating Fu Wenxuan Rao Hui Yang State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure Nanchang 330013JiangxiChina School of Electrical and Automation Engineering East China Jiaotong UniversityNanchang 330013JiangxiChina

The heavy-haul train(HHT)has large capacity and high efficiency,which represents the level of freight and makes the amelioration of control performance a trend in various countries.Improving the model reliability and increasing the anti-disturbance ability of the operation controller are two main ways to improve the operation control accuracy of HHTs.Herein,to describe the large nonlinear system more precisely,an interval type-2 fuzzy logic system(IT2FLS)is introduced to obtain a dynamic model.Then,a linear active disturbance rejection controller(LADRC)is designed to achieve precise operational control.In addition,the‘bandwidth method’is combinedwith the sparrowsearch algorithm(SSA)to solve the difficulty of controller parameters adjustment.Afterwards,the stability analysis of the closed-loop control system is given.The simulation experiments are conducted based on data collected from HXD1 locomotives driven by excellent drivers.Results show that the speed tracking error is no more than 0.5 km/h,and demonstrate that the proposed method significantly improves the operational performance of HHTs.

关键词： Heavy-haul train operational control IT2FLS LADRC Sparrow search algorithm