期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到865篇文章
< 1 2 … 44 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
A Firefly Algorithm-Optimized CNN-BiLSTM Model for Automated Detection of Bone Cancer and Marrow Cell Abnormalities 认领 引用
1
作者 Ishaani Priyadarshini 《Computers, Materials & Continua》 SCIE EI 2026年第3期1510-1535,共26页
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ... Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems. 展开更多
关键词 Firefly optimization algorithm(FO) marrow cell abnormalities bidirectional long short term memory(Bi-LSTM) temporal dependency modeling
暂未订购 下载PDF
Optimized VMD algorithm for noise reduction of absorption spectra of CO2 认领 引用
2
作者 Ru Zhang Junfen Wang +3 位作者 Xuemei Shi Mingliang Li Zhanmin Zhao Han Wang 《Optoelectronics Letters》 EI 2026年第1期23-28,共6页
The end tidal carbon dioxide(EtCO2)is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO2holds s... The end tidal carbon dioxide(EtCO2)is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO2holds significant importance in clinical practice.The measurements of EtCO2based on wavelength modulation-direct absorption spectroscopy(WM-DAS)had great advantages and the noise reduction of spectrum was very important.An optimized variational mode decomposition(VMD)algorithm improved by the dung beetle optimization algorithm and wavelet packet denoising algorithm was proposed to enhance the measurement accuracy of EtCO2concentration.The dung beetle optimization algorithm was used to obtain the optimal number of decomposition mode layers K and secondary penalty factorα.The optimal parameters were used to decompose the original transmitted light intensity signal with noise,and a series of intrinsic mode functions(IMFs)were obtained.Pearson correlation coefficient(R)was used to select the pure signal and the noisy signal,and the noisy signal was denoised by wavelet packet denoising algorithm.The transmitted light intensity signal was reconstructed by the signal processed by wavelet packet denoising algorithm and the pure signal.The results showed that the proposed algorithm could effectively remove the noise of signal of transmitted light intensity and improve the accuracy of concentration measurements of EtCO2. 展开更多
关键词 monitoring patients respiratory functionwhich Variational Mode Decomposition Wavelet Packet Denoising noise reduction spectrum end tidal carbon dioxide etco Dung Beetle Optimization Algorithm dung be lung ventilation
暂未订购 下载PDF
Design of Optimized Recognition Algorithm for Degraded Facial Images in Wireless Channels 认领 引用
3
作者 Jiefu Yu 《Journal of Electronic Research and Application》 2026年第7期20-29,共10页
The Design of the wireless channel-degraded face image optimization recognition algorithm aims to solve the problem of reduced image quality due to wireless transmission and thus reduce the drop in recognition accurac... The Design of the wireless channel-degraded face image optimization recognition algorithm aims to solve the problem of reduced image quality due to wireless transmission and thus reduce the drop in recognition accuracy. A new optimization recognition algorithm has been put forward, and at the same time, a way to preprocess the degraded image, enhance its features, and improve the clarity of the image has also been introduced. Many attributes and a combination of local and global features will be employed to increase the accuracy and robustness of the recognition. Based on the experiment, it can be seen that this algorithm has improved the recognition rate of all kinds of degraded facial image and has good application prospects;thus, it offers a new solution for face image recognition in wireless communication. 展开更多
关键词 Wireless channel Degraded facial image Optimized recognition algorithm
暂未订购 下载PDF
Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
4
作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
暂未订购 下载PDF
Physics-informed neural network optimized by particle swarm algorithm for accurate prediction of blast-induced peak particle velocity 认领 引用
5
作者 Lang Qiu Yujie Zhu +3 位作者 Chen Xu Gaofeng Ren Yingguo Hu Xiaoli Liu 《Intelligent Geoengineering》 2025年第3期126-140,共15页
Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV pred... Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R2enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications. 展开更多
关键词 Peak particle velocity Blast-induced vibration Particle Swarm Optimization algorithm Physics-informed neural network Prediction system
暂未订购 下载PDF
A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
6
作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
暂未订购 下载PDF
GSLDWOA: A Feature Selection Algorithm for Intrusion Detection Systems in IIoT 认领 引用
7
作者 Wanwei Huang Huicong Yu +3 位作者 Jiawei Ren Kun Wang Yanbu Guo Lifeng Jin 《Computers, Materials & Continua》 SCIE EI 2026年第1期2006-2029,共24页
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from... Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%. 展开更多
关键词 Industrial Internet of Things intrusion detection system feature selection whale optimization algorithm Gaussian mutation
暂未订购 下载PDF
Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
8
作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
暂未订购 下载PDF
Theory Evolution Optimization:A Metaheuristic Algorithm BaSed on Evolution Process of Theory 认领 引用
9
作者 Jiacong Liu Jiaze Tu +5 位作者 Chunguang Bi Huiling Chen Ali Asghar Heidari Hao Xie Lei Liu Yi Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1015-1060,共46页
Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the T... Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the Theory Evolution Optimization(TEO)–an efficient metaheuristic inspired by the evolution of scientific theory.TEO simulates the competitive,accumulative,and replacement processes among scientific hypotheses,mirroring the evolution from a hypothesis to an established scientific theory.The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms,including highly competitive champions such as EBOwithCMAR,LSHADE_cnEpSi,and LSHADE.Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test,with multiple comparisons managed by the Friedman test.Initially,TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions.TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications.Additionally,a binary TEO(BTEO)variant is introduced and applied to feature selection tasks in discrete space.Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems.The source codes for this research are accessible to the public at http://gffzze767f4cc5ce545d8s6q0ufvv9c5cn6vou.ffgz.tsg.suse.edu.cn/TEO.html. 展开更多
关键词 Optimization,metaheuristic algorithms Evolution of scientific theory Theory evolution optimizers Engineering design optimization Feature selection
Optimized Cardiovascular Disease Prediction Using Clustered Butterfly Algorithm 认领 引用 被引量：1
10
作者 Kamepalli S.L.Prasanna Vijaya J +2 位作者 Parvathaneni Naga Srinivasu Babar Shah Farman Ali 《Computers, Materials & Continua》 SCIE EI 2025年第10期1603-1630,共28页
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h... Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance. 展开更多
关键词 Cardiovascular disease prediction healthcare management system clustering RoughK-means classification butterfly optimization algorithm
暂未订购 下载PDF
PID Steering Control Method of Agricultural Robot Based on Fusion of Particle Swarm Optimization and Genetic Algorithm 认领 引用 被引量：4
11
作者 ZHAO Longlian ZHANG Jiachuang +2 位作者 LI Mei DONG Zhicheng LI Junhui 《农业机械学报》 EI CAS CSCD 北大核心 2026年第1期358-367,共10页
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion... Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots. 展开更多
关键词 agricultural robot steering PID control particle swarm optimization algorithm genetic algorithm
暂未订购 下载PDF
Optimization of Truss Structures Using Nature-Inspired Algorithms with Frequency and Stress Constraints 认领 引用 被引量：1
12
作者 Sanjog Chhetri Sapkota Liborio Cavaleri +3 位作者 Ajaya Khatri Siddhi Pandey Satish Paudel Panagiotis G.Asteris 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期436-464,共29页
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru... Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior. 展开更多
关键词 Optimization truss structures nature-inspired algorithms meta-heuristic algorithms red kite opti-mization algorithm secretary bird optimization algorithm
暂未订购 下载PDF
An Interpretable AI Framework for Predicting Groundwater Contamination under Atmospheric and Industrial Pollution Using Metaheuristic-Optimized Deep Learning 认领 引用
13
作者 Md.Mottahir Alam Mohammed K.Al Mesfer +3 位作者 Haroonhaider Sidhwa Mohd Danish Asif Irshad Khan Tauheed Khan Mohd 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期750-783,共34页
Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especially... Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios. 展开更多
关键词 Groundwater quality prediction interpretable artificial intelligence industrial and atmospheric pollution spatial-temporal-assisted Deep Belief Network Tiki-Taka Algorithm Addax Optimization Algorithm Whale Optimization Algorithm
暂未订购 下载PDF
Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
14
作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
暂未订购 下载PDF
A Hybrid Approach for Query-Based Data Extraction Using Ensemble BERT Model with Walrus Optimization Algorithm 认领 引用
15
作者 Poluru Eswaraiah Uddagiri Sirisha +3 位作者 Shaik Abdul Nabi Revathi Durgam Pallavi Malavath Gilakara Muni Nagamani 《Computers, Materials & Continua》 SCIE EI 2026年第8期1580-1606,共27页
The growing volume of digital text complicates the extraction of relevant information from unstructured data.Transformer models such as BERT,ALBERT,and RoBERTa are powerful,but they may face challenges in hyperparamet... The growing volume of digital text complicates the extraction of relevant information from unstructured data.Transformer models such as BERT,ALBERT,and RoBERTa are powerful,but they may face challenges in hyperparameter optimization and adaptation to new domains.To address this issue,a hybrid ensemble BERT model is suggested,optimized using the Walrus Optimization Algorithm(WaOA).The framework applies PCA to reduce dimensionality,ontology normalization,and K-means clustering to improve semantic comprehension.Experimental results on the SQuAD 2.0 and MS MARCO datasets show that the proposed model outperforms the baseline models.WaOA(Weighted Average of Attention)can improve convergence,reduce training time,and enhance prediction accuracy.The model also improves the semantic relevance of the extracted information.Attention maps visualize the model’s focus on relevant query terms.The method enhances efficiency and cuts redundancy.It also provides a more generalized approach to different query types.The framework promotes consistent and reliable performance across different data conditions,including varying input formats and varying noise levels.It can be generalized to multilingual and domain-specific applications.Overall,the framework provides a scalable and reliable solution to real-world information extraction. 展开更多
关键词 Query-based information extraction ensemble BERT walrus optimization algorithm metaheuristic learning PCA K-means clustering ROUGE t-SNE attention visualization
暂未订购 下载PDF
Integrated diagnosis of abnormal energy consumption in converter steelmaking using GWO-SVM-K-means algorithms 认领 引用
16
作者 Fei-Xiang Dai Xiang-Jun Bao +2 位作者 Lu Zhang Xiao-Jing Yang Guang Chen 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第1期458-468,共11页
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ... To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency. 展开更多
关键词 Converter smelting process Abnormal energy diagnosis Gray wolf optimization algorithm Support vector machine K-means clustering algorithm
暂未订购 下载PDF
MCPSFOA:Multi-Strategy Enhanced Crested Porcupine-Starfish Optimization Algorithm for Global Optimization and Engineering Design 认领 引用
17
作者 Hao Chen Tong Xu +2 位作者 Yutian Huang Dabo Xin Changting Zhong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期494-545,共52页
Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(... Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems. 展开更多
关键词 Global optimization starfish optimization algorithm crested porcupine optimizer metaheuristic Gaussian mutation population diversity enhancement
暂未订购 下载PDF
MWaOA:A Bio-Inspired Metaheuristic Algorithm for Resource Allocation in Internet of Things 认领 引用
18
作者 Rekha Phadke Abdul Lateef Haroon Phulara Shaik +3 位作者 Dayanidhi Mohapatra Doaa Sami Khafaga Eman Abdullah Aldakheel N.Sathyanarayana 《Computers, Materials & Continua》 SCIE EI 2026年第2期1285-1310,共26页
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ... Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters. 展开更多
关键词 Delay gateway internet of things resource allocation resource management walrus optimization algorithm
暂未订购 下载PDF
Propagation Alongside Crossover:An Evolutionary Algorithm for Continuous Optimization and Feature Selection 认领 引用
19
作者 Najibeh Farzi-Veijouyeh Vahideh Sahargahi Neda Matin 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1112-1175,共64页
Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(... Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(PAC)algorithm to address continuous optimization challenges.The primary goal of PAC is to structure the algorithmic phases in a manner that achieves a robust balance between exploration and exploitation through appropriately designed mechanisms at each stage.PAC simultaneously leverages the benefits of propagation,crossover,and mutation.Three independent operators are defined to generate new candidate solutions separately,and a novel selection strategy allows individuals produced by each operator,along with members of the current population,to independently enter the next generation.This design preserves population diversity,prevents all individuals from converging toward a single point,and enhances the algorithm’s ability to explore the solution space effectively.A key innovation of PAC is its three-mode propagation mechanism,which comprises local search,linear propagation toward the target point,and tear-drop shaped propagation toward the target point.Tear-drop propagation provides a precise and adaptive search around promising solutions,increasing diversity and preventing entrapment in local optima.The target point is typically set as the global optimum;however,when propagating the global optimum itself,a random point is used as the target to further enhance exploration and escape from local optima.The initial population is generated using chaotic mapping to ensure broad coverage of the search space.PAC was rigorously evaluated on 51 benchmark functions and three engineering problems,considering scalability,convergence,sensitivity,and computational efficiency.Comparative analyses with established optimization algorithms demonstrate PAC’s superior performance,as confirmed by Wilcoxon signed-rank and Friedman statistical tests.Furthermore,PAC was applied as a feature selection method on four diverse datasets,achieving substantial dimensionality reduction while outperforming comparative methods in classification accuracy.These results highlight PAC’s versatility,robustness,and practical effectiveness. 展开更多
关键词 Optimization algorithms Meta-heuristic algorithms Continuous optimization Propagation alongside crossover algorithm Intrusion detection
The Optimization Design of Particle-Reinforced Composite Materials Based on the Artificial Fish Swarm Algorithm and Voronoi Cell Finite Element Method 认领 引用
20
作者 Peng Zhang Ran Guo 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第3期316-328,共13页
In this study,a collaborative optimization framework combining artificial fish swarm algorithm(AFSA)and Voronoi cell finite element method(VCFEM)is proposed to solve the problem of the influence of microstructure dist... In this study,a collaborative optimization framework combining artificial fish swarm algorithm(AFSA)and Voronoi cell finite element method(VCFEM)is proposed to solve the problem of the influence of microstructure distribution on the macroscopic mechanical properties of particle-reinforced composites.Compared with the traditional displacement-based finite element method,VCFEM significantly improves the computational efficiency of multi-inclusion problems by means of generalized stress function and element local adaptation technology,and accurately captures the stress field discontinuity and interface stress concentration phenomenon.At the same time,AFSA abandons the dependence of traditional optimization methods on simplified models,and directly searches for the global optimal solution through discrete topological variables(particle positions).Its natural selection mechanism and swarm intelligence characteristics effectively overcome the local optimal trap.The numerical simulation results show that the proposed method exhibits high accuracy in both single-and multi-inclusion models(the error with the commercial software MARC is less than 5%),and in the complex model with 100 inclusions,the maximum Mises stress is reduced by 32.6%after optimization.The synergistic effect of the two breaks through the trade-off between efficiency and accuracy in traditional optimization,and provides the ability of both computational efficiency and global optimization for multi-scale composite material design. 展开更多
关键词 Particle-reinforced composites Artificial fish swarm algorithm Voronoi cell finite element method Optimization algorithms
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 44 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈