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Optimization model for performance-based warranty decision of degraded systems based on improved sparrow search algorithm 认领 引用
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作者 DONG Enzhi CHENG Zhonghua +3 位作者 LIU Zichang ZHU Xi WANG Rongcai BAI Yongsheng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1259-1280,共22页
Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.Th... Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings. 展开更多
关键词 performance-based warranty gamma process periodic inspection intelligent optimization algorithm
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Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states 认领 引用 被引量：2
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作者 程鑫 鲁秀娟 +1 位作者 刘亚楠 匡森 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第2期53-59,共7页
Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), ... Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered.This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool. 展开更多
关键词 quantum control state preparation intelligent optimization algorithm
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Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods 认领 引用
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作者 Musaed Alrashidi 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期491-513,共23页
Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and sha... Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy. 展开更多
关键词 Weibull distribution conventional numerical methods intelligent optimization algorithms wind resource exploration and exploitation cost of energy($/kWh)
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Optimal Search Mechanism Analysis of Light Ray Optimization Algorithm 认领 引用
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作者 Jihong SHEN Jialian LI Bin WEI 《Journal of Mathematical Research with Applications》 CSCD 2012年第5期530-542,共13页
Based on Fermat＇s principle and the automatic optimization mechanism in the propagation process of light, an optimal searching algorithm named light ray optimization is presented, where the laws of refraction and ref... Based on Fermat＇s principle and the automatic optimization mechanism in the propagation process of light, an optimal searching algorithm named light ray optimization is presented, where the laws of refraction and reflection of light rays are integrated into searching process of optimization. In this algorithm, coordinate space is assumed to be the space that is full of media with different refractivities, then the space is divided by grids, and finally the searching path is assumed to be the propagation path of light rays. With the law of refraction, the search direction is deflected to the direction that makes the value of objective function decrease. With the law of reflection, the search direction is changed, which makes the search continue when it cannot keep going with refraction. Only the function values of objective problems are used and there is no artificial rule in light ray optimization, so it is simple and easy to realize. Theoretical analysis and the results of numerical experiments show that the algorithm is feasible and effective. 展开更多
关键词 Fermat＇s principle intelligent optimization algorithm light ray optimization optimal search mechanism.
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An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems 认领 引用
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作者 L. Q. Zhang J. Xiong J. K. Liu 《Journal of Applied Mathematics and Physics》 2023年第11期3641-3650,共10页
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ... In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance. 展开更多
关键词 Swarm Intelligent Optimization Algorithm Fruit Fly Optimization Algorithm Adaptive Step Local Optimum Convergence Speed
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Intelligent prediction of deep rock strength based on modified three-dimensional Hoek-Brown criterion 认领 引用 被引量：1
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作者 Jiaxin Wang Hongru Jiang +4 位作者 Shunchuan Wu Shihuai Zhang Chaoqun Chu Xiaoping Zhang Yingming Xiao 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第7期2454-2472,I0020-I0030,共19页
To study the deep rock strength,this paper proposes a five-parameter deviatoric function to modify the deviatoric function of the Hoek-Brown(HB)criterion introduces an intelligent optimization algorithm(IOA)to determi... To study the deep rock strength,this paper proposes a five-parameter deviatoric function to modify the deviatoric function of the Hoek-Brown(HB)criterion introduces an intelligent optimization algorithm(IOA)to determine the material parameters,thereby constructing a modified three-dimensional(3D)HB criterion,namely MMCHB criterion.The MMCHB criterion avoids the defects of the traditional HB criterion,which neither considers the Intermediate principal stress(IPS)nor meets the smoothness requirement,and overcomes the shortcomings of parameter determination based on conventional methods,which can lead to a single deviatoric plane envelope shape.This modified criterion can be degenerated into the HB criterion under triaxial compression and tension.The proposed criterion is verified using true triaxial test data for six types of intact rock,and the modified 3D HB criteria are selected for comparative study.The results show that the proposed criterion under the IOA has the best prediction error for the six rock types,ranging from 1.6636% to 3.4023%.Overall,the MMCHB criterion outperforms the existing modified 3D HB criteria in prediction.Based on the proposed MMCHB criterion,an intelligent prediction system is developed,which provides a new approach for intelligent prediction of deep rock strength and dynamic construction of rock material parameters. 展开更多
关键词 rock strength Hoek-Brown criterion Intermediate principal stress intelligent optimization algorithm intelligent predictionsystem
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Research on the Application of Intelligent Optimization Algorithm in Mechanical Design 认领 引用
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作者 Donglai LUAN 《Mechanical Engineering Science》 2024年第1期26-29,共4页
Intelligent optimization algorithm belongs to a kind of emerging technology,show good characteristics,such as high performance,applicability,its algorithm includes many contents,including genetic,particle swarm and ar... Intelligent optimization algorithm belongs to a kind of emerging technology,show good characteristics,such as high performance,applicability,its algorithm includes many contents,including genetic,particle swarm and artificial neural network algorithm,compared with the traditional optimization way,these algorithms can be applied to a variety of situations,meet the demand of solution,in the mechanical design industry has wide application prospects.This paper analyzes the application of the algorithm in mechanical design and the comparison of the results to verify the significance of the intelligent optimization algorithm in mechanical design. 展开更多
关键词 intelligent optimization algorithm mechanical design application
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Product quality prediction based on RBF optimized by firefly algorithm 认领 引用 被引量：4
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作者 HAN Huihui WANG Jian +1 位作者 CHEN Sen YAN Manting 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第1期105-117,共13页
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred... With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality. 展开更多
关键词 product quality prediction data pre-processing radial basis function swarm intelligence optimization algorithm
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Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms 认领 引用
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作者 Zhonghai Jiang Qian Wang +1 位作者 Liangbing Zhou Chun Xiao 《Fluid Dynamics & Materials Processing》 EI 2023年第6期1693-1708,共16页
A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.... A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model. 展开更多
关键词 Asphalt pavement temperature field swarm intelligence optimization algorithm prediction
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Review on the Optimal Design of Cyclone Separator:Theory,Methodology, and Applications 认领 引用
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作者 Bin Li Liying Gao +4 位作者 Yong Li Kun Zhu Zhenling Fu Shifan Xu Mohan Li 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第3期43-71,共29页
Cyclone separators are highly efficient gas-solid separation that operate on the centrifugal force and play an indispensable role in industries such as chemical engineering,environmental protection,and power generatio... Cyclone separators are highly efficient gas-solid separation that operate on the centrifugal force and play an indispensable role in industries such as chemical engineering,environmental protection,and power generation.They exhibit excellent reliability,particularly under demanding conditions such as high temperatures and elevated particle concentrations.However,a persistent trade-off between separation efficiency and pressure drop has limited further performance improvements.To address this,optimization of cyclone separators has become a major research focus.This article systematically reviews recent advances,first by examining the mechanisms through which key structural parameters,such as inlet geometry,exhaust pipe diameter,and cone angle,influence performance across different industrial applications.Furthermore,the review introduces an integrated optimization framework based on computational fluid dynamics(CFD)simulations,surrogate modelling,and intelligent optimization algorithms to enhance design performance.It critically compares the applicability and limitations of various high-dimensional optimization methods and their integration strategies.The article underscores a paradigm shift from optimizing instantaneous performance toward establishing a lifecycle optimization(LCO)framework that incorporates longterm metrics such as wear and maintenance costs.Evidence shows that coupling CFD with intelligent algorithms enables efficient exploration of multi-objective parameter spaces.Finally,the article discusses current limitations in optimization research and outlines future directions,including multi-physics coupling involving flow,heat,and particle transport,lifecycle optimization,and intelligent decision support systems.In summary,this review establishes a theoretical foundation and provides technical guidance for the energy-efficient,high-performance design and industrial implementation of cyclone separators. 展开更多
关键词 Cyclone separator CFD surrogate model heat transfer intelligent optimization algorithm
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Interpretable CEEMDAN-SMA-LSSVM hybrid model for predicting shield tunnel-induced settlement 认领 引用
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作者 Shaoqiang Meng Zhenming Shi Marte Gutierrez 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第10期6179-6194,共16页
Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid mode... Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid model that combines complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),slime mold algorithm(SMA),and least squares support vector machine(LSSVM)to enhance prediction accuracy and model transparency.The CEEMDAN method,optimized by SMA,decomposes settlement data into intrinsic mode functions(IMFs)and residuals,thereby reducing data noise.The LSSVM,also optimized by SMA,is then applied to predict each IMF and residual.The final settlement prediction is derived from the aggregation of these results.The model was rigorously validated using the Changsha(China)and Singapore Metro projects,demonstrating superior performance to traditional machine learning models.The evaluation metrics,including root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2),underscore the model's effectiveness.The model achieved the lowest error rates and highest accuracy across these metrics.Notably,Shapley additive explanations(SHAP)provided insights into the model's decision-making process,identifying shield stoppage and moisture content as the most influential factors in settlement prediction.This study contributes to the advancement of the methodological framework for predicting tunnel settlement.It addresses the discrepancy between prediction accuracy and interpretability,providing a robust tool for practical engineering applications. 展开更多
关键词 Machine learning Geotechnical engineering Swarm intelligence optimization algorithm Shapley additive explanations(SHAP)
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Design and Optimization of Solar Photovoltaic Brackets in Mountainous Areas Adapted to Complex Wind Environments 认领 引用 被引量：1
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作者 Aijun LIN Junwen TANG 《Asian Agricultural Research》 2024年第12期22-25,共4页
With the increasing global demand for renewable energy,the application of photovoltaic power generation in mountainous areas is gradually increasing.However,the complex wind environment in mountainous areas poses seve... With the increasing global demand for renewable energy,the application of photovoltaic power generation in mountainous areas is gradually increasing.However,the complex wind environment in mountainous areas poses severe challenges to the design and optimization of solar photovoltaic brackets.Traditional design methods are difficult to cope with the changeable wind speed and direction in mountainous areas,resulting in structural instability or material waste.Researchers have identified the key factors affecting wind response through parametric research and dynamic wind response analysis,so as to optimize the brackets design and improve its adaptability and stability in complex wind environments.In this paper,the complexity of wind speed,wind direction and turbulence characteristics in mountainous areas and their influence on brackets design are explored.Through static and dynamic wind load analysis,the geometrical shape and material selection of the bracket are optimized to enhance its wind resistance.The application of multi-objective optimization model and intelligent optimization algorithm provides an effective solution for the design of solar photovoltaic brackets,ensuring their safety and reliability in complex wind environments. 展开更多
关键词 Solar photovoltaic brackets Complex wind environment Optimization design Intelligent optimization algorithm
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A study on the credit risk of commercial banks based on intelligent optimization algorithms to modify the KMV model 认领 引用
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作者 Chaojie Fan 《Journal of Applied Economics and Policy Studies》 2025年第6期1-9,共9页
Credit risk is one of the main risks faced by commercial banks.Credit risk management includes risk identification,assessment,and early warning,among which risk assessment is fundamental and key.Currently,research on ... Credit risk is one of the main risks faced by commercial banks.Credit risk management includes risk identification,assessment,and early warning,among which risk assessment is fundamental and key.Currently,research on credit risk assessment in China is still in its developing stage,and the precision of measuring credit risk needs improvement.Among various evaluation methods,the Kealhofer,McQuown,and Vasicek model(KMV model)has shown good practical application and is relatively suitable for the national conditions of China.However,it still has some flaws.To address the issue of insufficient external validity in the default point parameter settings of the KMV model,the Particle Swarm Optimization(PSO)algorithm is used to optimize these parameters,and the Particle Swarm Optimization-Grey Wolf Optimization(PSO-GWO)algorithm is integrated to construct the Adaptive Particle Swarm Optimization-KMV Model(APSO-KMV model)and the PSO-GWO-KMV model.Based on an empirical study comparing real data from 5,234 companies,it was found that the original KMV model had an Area Under the Curve(AUC)value of 0.7362,accuracy of 0.2610,and binary cross-entropy loss of 0.7006;the PSO-KMV model had a shortterm debt coefficient?of 0.0496,a long-term debt coefficient?of 0.2508,an AUC value of 0.9994,accuracy of 0.9996,and binary cross-entropy loss of 4.1990;the PSO-GWO-KMV model had a coefficient?of 0.0496 and a value?of 0.2690,an AUC value of 0.9987,accuracy of 0.7603,and binary cross-entropy loss of 4.0804.The optimized KMV model showed a significant improvement in predictive accuracy. 展开更多
关键词 commercial banks credit risk KMV model intelligent optimization algorithms
VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity 认领 引用
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作者 Junqiang Jiang Zhifang Sun +3 位作者 Xiong Jiang Shengjie Jin Yinli Jiang Bo Fan 《Computers, Materials & Continua》 SCIE EI 2023年第11期1617-1644,共28页
The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple pr... The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value. 展开更多
关键词 Intelligence optimization algorithm grey wolf optimizer(GWO) manhattan distance symmetric coordinates
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Nonlinear Equations Solving with Intelligent Optimization Algorithms: A Survey 认领 引用 被引量：23
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作者 Wenyin Gong Zuowen Liao +2 位作者 Xianyan Mi Ling Wang Yuanyuan Guo 《Complex System Modeling and Simulation》 2021年第1期15-32,共18页
Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it ... Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it is a difficult and challenging task in numerical computation.In recent years,Intelligent Optimization Algorithms(IOAs)have shown to be particularly effective in solving NEs.This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs.This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques.Then,solving NEs with IOAs is reviewed,followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms.Finally,this paper points out the challenges and some possible open issues for solving NEs. 展开更多
关键词 Nonlinear Equations(NEs) Intelligent Optimization Algorithms(IOA) multiple roots location transformation techniques diversity preservation
New Optimization Design Method for a Double Secondary Linear Motor Based on R-DNN Modeling Method and MCS Optimization Algorithm 认领 引用 被引量：7
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作者 Weitao Wang Jiwen Zhao +1 位作者 Yang Zhou Fei Dong 《Chinese Journal of Electrical Engineering》 EI CSCD 2020年第3期98-105,共8页
Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization algorithms.However,this approach has disadvantages,e.g.,the analytical model might not be accurate e... Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization algorithms.However,this approach has disadvantages,e.g.,the analytical model might not be accurate enough,and the intelligent optimization algorithm can easily fall into local optimization.A new linear motor optimization strategy combining an R-deep neural network(R-DNN)and modified cuckoo search(MCS)is proposed;additionally,the thrust lifting and thrust fluctuation reductions are regarded as optimization objectives.The R-DNN is a deep neural network modeling method using the rectified linear unit(RELU)activation function,and the MCS provides a faster convergence speed and stronger data search capability as compared with genetic algorithms,particle swarm optimization,and standard CS algorithms.Finally,the validity and accuracy of this work are proven based on prototype experiments. 展开更多
关键词 Double secondary linear motor(DSLM) machine learning modeling R-deep neural network(R-DNN)algorithm intelligent optimization algorithm modified cuckoo search(MCS)algorithm
A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification 认领 引用
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作者 Tsu-Yang Wu Haonan Li +1 位作者 Saru Kumari Chien-Ming Chen 《Computers, Materials & Continua》 SCIE EI 2024年第4期19-46,共28页
Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convol... Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification. 展开更多
关键词 Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification
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A Clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations 认领 引用 被引量：1
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作者 Zhou Yaqiong Chen Junqi +2 位作者 Li Weishi Qiu Sihang Ju Rusheng 《系统仿真学报》 CAS CSCD 北大核心 2024年第12期2782-2796,共15页
To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al... To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm. 展开更多
关键词 location problem clustering algorithm intelligent optimization algorithm Pareto front
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Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on Dung Beetle Optimizer 认领 引用
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作者 Baihong TONG Jinming LIU Jianfei SHI 《Agricultural Biotechnology》 2024年第5期83-85,92,共3页
Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn s... Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn stalks,and it is very important to determine its content in corn stalks.In this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was studied.In order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of NIRS.Its modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms. 展开更多
关键词 Hemicellulose Near-infrared spectrum Characteristic wavelength selection Intelligent optimization algorithm Dung beetle algorithm
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Artificial Intelligence Methods Applied to Catalytic Cracking Processes 认领 引用 被引量：2
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作者 Fan Yang Mao Xu +1 位作者 Wenqiang Lei Jiancheng Lv 《Big Data Mining and Analytics》 EI CSCD 2023年第3期361-380,共20页
Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning a... Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in FCC.This paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up research.Compared with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large delay.AI methods applied in product yield analysis build models based on massive data.By fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model accuracy.AI methods applied in flue gas desulfurization can be usually divided into two stages:modeling and optimization.In the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base.AI methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition warning.Knowledge-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction recall.While the data-driven methods are just the opposite.Thus,some studies combine these two methods to obtain better results. 展开更多
关键词 neural networks intelligent optimization algorithm catalytic cracking lumped kinetics
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