Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task tr...Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is proposed.Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning model.And combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency.Meanwhile,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using ma...The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning models.Analysis of AD using EEG involves multi-channel analysis.However,the use of multiple channels may impact the classification performance due to data redundancy and complexity.In this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition methods.Empirical Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG analysis.We extracted thirty-four features from each subband of EEG signals.Finally,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel selection.The effectiveness of this model is assessed by two publicly accessible AD EEG datasets.An accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG channels.Moreover,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)channels.The performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.展开更多
With the development of composite materials,their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors,significantly r...With the development of composite materials,their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors,significantly reducing the energy consumption during the operation of EMUs(Electric Multiple Units).This study aims to explore the application of composite materials in the lightweight design of EMU front skirts and proposes a design method based on threedimensional Hashin failure criteria and the Cheetah Optimizer(CO)to achieve maximum lightweight efficiency.The UMAT subroutine was developed based on the three-dimensional Hashin failure criteria to calculate failure parameters,which were used as design parameters in the CO.The model calculations and result extraction were implemented in MATLAB,and the Cheetah Optimizer iteratively determined the optimal laminating angle design that minimized the overall failure factor.After 100 iterations,ensuring structural integrity,the optimized design reduced the weight of the skirt panel by 60% compared to the original aluminum alloy structure,achieving significant lightweight benefits.This study provides foundational data for the lightweight design of EMUs.展开更多
Assessing the stability of slopes is one of the crucial tasks of geotechnical engineering for assessing and managing risks related to natural hazards,directly affecting safety and sustainable development.This study pr...Assessing the stability of slopes is one of the crucial tasks of geotechnical engineering for assessing and managing risks related to natural hazards,directly affecting safety and sustainable development.This study primarily focuses on developing robust and practical hybrid models to predict the slope stability status of circular failure mode.For this purpose,three robust models were developed using a database including 627 case histories of slope stability status.The models were developed using the random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)techniques,employing 5-fold cross validation approach.To enhance the performance of models,this study employs Bayesian optimizer(BO)to fine-tuning their hyperparameters.The results indicate that the performance order of the three developed models is RF-BO>SVM-BO>XGB-BO.Furthermore,comparing the developed models with previous models,it was found that the RF-BO model can effectively determine the slope stability status with outstanding performance.This implies that the RF-BO model could serve as a dependable tool for project managers,assisting in the evaluation of slope stability during both the design and operational phases of projects,despite the inherent challenges in this domain.The results regarding the importance of influencing parameters indicate that cohesion,friction angle,and slope height exert the most significant impact on slope stability status.This suggests that concentrating on these parameters and employing the RF-BO model can effectively mitigate the severity of geohazards in the short-term and contribute to the attainment of long-term sustainable development objectives.展开更多
Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the l...Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the literature.However,chaos theory has not been extensively investigated in AO.Moreover,it is still not applied in the parameter estimation of electro-hydraulic systems.In this work,ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique.An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation(CEC)functions shows that chaotic Aquila optimization techniques perform better than the baseline technique.The investigation is further conducted on parameter estimation of an electro-hydraulic control system,which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map(CAO6)achieves the best fitness values of and at noise levels and respectively.Friedman test 2.873E-05,1.014E-04,8.728E-031.300E-03,1.300E-02,1.300E-01,for repeated measures,computational analysis,and Taguchi test reflect the superiority of CAO6 against the state of the arts,demonstrating its potential for addressing various engineering optimization problems.However,the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios.展开更多
Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecti...Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.展开更多
Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricac...Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.展开更多
With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impu...With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.展开更多
Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g...Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.展开更多
This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narw...This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.展开更多
As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for th...As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for these algorithms.In this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization challenges.It employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy search.Key parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational efficiency.We offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and independent.RA is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000.The results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and scalability.Finally,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.展开更多
The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid different...The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid differential evolution(DE)algorithm combined with an improved equilibrium optimizer(DE-IEO)specifically for the CHPED problem.The DE-IEO incorporates three enhancement strategies:a chaotic mechanism for initializing the population,an improved equilibrium pool strategy,and a quasi-opposite based learning mechanism.These strategies enhance the individual utilization capabilities of the equilibrium optimizer,while differential evolution boosts local exploitation and escape capabilities.The IEO enhances global search to enrich the solution space,and DE focuses on local exploitation for more accurate solutions.The effectiveness of DE-IEO is demonstrated through comparative analysis with other metaheuristic optimization algorithms,including PSO,DE,ABC,GWO,WOA,SCA,and equilibrium optimizer(EO).Additionally,improved algorithms such as the enhanced chaotic gray wolf optimization(ACGWO),improved particle swarm with adaptive strategy(MPSO),and enhanced SCA with elite and dynamic opposite learning(EDOLSCA)were tested on the CEC2017 benchmark suite and four CHPED systems with 24,84,96,and 192 units,respectively.The results indicate that the proposed DE-IEO algorithm achieves satisfactory solutions for both the CEC2017 test functions and real-world CHPED optimization problems,offering a viable approach to complex optimization challenges.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.T...Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.展开更多
Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things(SCADA-IIoT)systems against intruders has become essential since industrial control systems now oversee critical infrastructure,and cybe...Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things(SCADA-IIoT)systems against intruders has become essential since industrial control systems now oversee critical infrastructure,and cyber attackers more frequently target these systems.Due to their connection of physical assets with digital networks,SCADA-IIoT systems face substantial risks from multiple attack types,including Distributed Denial of Service(DDoS),spoofing,and more advanced intrusion methods.Previous research in this field faces challenges due to insufficient solutions,as current intrusion detection systems lack the necessary accuracy,scalability,and adaptability needed for IIoT environments.This paper introduces CyberFortis,a novel cybersecurity framework aimed at detecting and preventing cyber threats in SCADA-IIoT systems.CyberFortis presents two key innovations:Firstly,Siamese Double Deep Q-Network with Autoencoders(Siamdqn-AE)FusionNet,which enhances intrusion detection by combining deep Q-Networks with autoencoders for improved attack detection and feature extraction;and secondly,the PopHydra Optimiser,an innovative solution to compute reinforcement learning discount factors for better model performance and convergence.This method combines Siamese deep Q-Networks with autoencoders to create a system that can detect different types of attacks more effectively and adapt to new challenges.CyberFortis is better than current top attack detection systems,showing higher scores in important areas like accuracy,precision,recall,and F1-score,based on data from CICIoT 2023,UNSW-NB 15,and WUSTL-IIoT datasets.Results from the proposed framework show a 97.5%accuracy rate,indicating its potential as an effective solution for SCADA-IIoT cybersecurity against emerging threats.The research confirms that the proposed security and resilience methods are successful in protecting vital industrial control systems within their operational environments.展开更多
Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexi...Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.展开更多
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke...The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.展开更多
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金funded by the Jiangxi Provincial Social Science Planning Project(21GL12)Jiangxi Provincial Higher Education Humanities and Social Sciences Planning Project(GL22232)Jiangxi Province College Students’Innovation and Entrepreneurship Training Program Project(S20241041027).
文摘Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is proposed.Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning model.And combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency.Meanwhile,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
文摘The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning models.Analysis of AD using EEG involves multi-channel analysis.However,the use of multiple channels may impact the classification performance due to data redundancy and complexity.In this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition methods.Empirical Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG analysis.We extracted thirty-four features from each subband of EEG signals.Finally,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel selection.The effectiveness of this model is assessed by two publicly accessible AD EEG datasets.An accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG channels.Moreover,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)channels.The performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.
文摘With the development of composite materials,their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors,significantly reducing the energy consumption during the operation of EMUs(Electric Multiple Units).This study aims to explore the application of composite materials in the lightweight design of EMU front skirts and proposes a design method based on threedimensional Hashin failure criteria and the Cheetah Optimizer(CO)to achieve maximum lightweight efficiency.The UMAT subroutine was developed based on the three-dimensional Hashin failure criteria to calculate failure parameters,which were used as design parameters in the CO.The model calculations and result extraction were implemented in MATLAB,and the Cheetah Optimizer iteratively determined the optimal laminating angle design that minimized the overall failure factor.After 100 iterations,ensuring structural integrity,the optimized design reduced the weight of the skirt panel by 60% compared to the original aluminum alloy structure,achieving significant lightweight benefits.This study provides foundational data for the lightweight design of EMUs.
文摘Assessing the stability of slopes is one of the crucial tasks of geotechnical engineering for assessing and managing risks related to natural hazards,directly affecting safety and sustainable development.This study primarily focuses on developing robust and practical hybrid models to predict the slope stability status of circular failure mode.For this purpose,three robust models were developed using a database including 627 case histories of slope stability status.The models were developed using the random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)techniques,employing 5-fold cross validation approach.To enhance the performance of models,this study employs Bayesian optimizer(BO)to fine-tuning their hyperparameters.The results indicate that the performance order of the three developed models is RF-BO>SVM-BO>XGB-BO.Furthermore,comparing the developed models with previous models,it was found that the RF-BO model can effectively determine the slope stability status with outstanding performance.This implies that the RF-BO model could serve as a dependable tool for project managers,assisting in the evaluation of slope stability during both the design and operational phases of projects,despite the inherent challenges in this domain.The results regarding the importance of influencing parameters indicate that cohesion,friction angle,and slope height exert the most significant impact on slope stability status.This suggests that concentrating on these parameters and employing the RF-BO model can effectively mitigate the severity of geohazards in the short-term and contribute to the attainment of long-term sustainable development objectives.
基金funded by Taif University,Saudi Arabia,Project No.(TU-DSPP-2024-52).
文摘Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the literature.However,chaos theory has not been extensively investigated in AO.Moreover,it is still not applied in the parameter estimation of electro-hydraulic systems.In this work,ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique.An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation(CEC)functions shows that chaotic Aquila optimization techniques perform better than the baseline technique.The investigation is further conducted on parameter estimation of an electro-hydraulic control system,which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map(CAO6)achieves the best fitness values of and at noise levels and respectively.Friedman test 2.873E-05,1.014E-04,8.728E-031.300E-03,1.300E-02,1.300E-01,for repeated measures,computational analysis,and Taguchi test reflect the superiority of CAO6 against the state of the arts,demonstrating its potential for addressing various engineering optimization problems.However,the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios.
基金Researchers supporting Project number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
文摘Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.
基金the Science and Technology Commission of Shanghai Municipality(No.19030501100)the Technical Service Platform for Vibration and Noise Testing and Control of New Energy Vehicles(No.18DZ2295900)。
文摘With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
文摘This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.
文摘As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for these algorithms.In this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization challenges.It employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy search.Key parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational efficiency.We offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and independent.RA is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000.The results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and scalability.Finally,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.
基金supported by the Scientific Research Project of Xiangsihu College of Guangxi Minzu University,Grant No.2024XJKY06the National Natural Science Foundation of China under Grant No.U21A20464.
文摘The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid differential evolution(DE)algorithm combined with an improved equilibrium optimizer(DE-IEO)specifically for the CHPED problem.The DE-IEO incorporates three enhancement strategies:a chaotic mechanism for initializing the population,an improved equilibrium pool strategy,and a quasi-opposite based learning mechanism.These strategies enhance the individual utilization capabilities of the equilibrium optimizer,while differential evolution boosts local exploitation and escape capabilities.The IEO enhances global search to enrich the solution space,and DE focuses on local exploitation for more accurate solutions.The effectiveness of DE-IEO is demonstrated through comparative analysis with other metaheuristic optimization algorithms,including PSO,DE,ABC,GWO,WOA,SCA,and equilibrium optimizer(EO).Additionally,improved algorithms such as the enhanced chaotic gray wolf optimization(ACGWO),improved particle swarm with adaptive strategy(MPSO),and enhanced SCA with elite and dynamic opposite learning(EDOLSCA)were tested on the CEC2017 benchmark suite and four CHPED systems with 24,84,96,and 192 units,respectively.The results indicate that the proposed DE-IEO algorithm achieves satisfactory solutions for both the CEC2017 test functions and real-world CHPED optimization problems,offering a viable approach to complex optimization challenges.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
文摘Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.
基金financially supported by the Ongoing Research Funding Program(ORF-2025-846),King Saud University,Riyadh,Saudi Arabia.
文摘Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things(SCADA-IIoT)systems against intruders has become essential since industrial control systems now oversee critical infrastructure,and cyber attackers more frequently target these systems.Due to their connection of physical assets with digital networks,SCADA-IIoT systems face substantial risks from multiple attack types,including Distributed Denial of Service(DDoS),spoofing,and more advanced intrusion methods.Previous research in this field faces challenges due to insufficient solutions,as current intrusion detection systems lack the necessary accuracy,scalability,and adaptability needed for IIoT environments.This paper introduces CyberFortis,a novel cybersecurity framework aimed at detecting and preventing cyber threats in SCADA-IIoT systems.CyberFortis presents two key innovations:Firstly,Siamese Double Deep Q-Network with Autoencoders(Siamdqn-AE)FusionNet,which enhances intrusion detection by combining deep Q-Networks with autoencoders for improved attack detection and feature extraction;and secondly,the PopHydra Optimiser,an innovative solution to compute reinforcement learning discount factors for better model performance and convergence.This method combines Siamese deep Q-Networks with autoencoders to create a system that can detect different types of attacks more effectively and adapt to new challenges.CyberFortis is better than current top attack detection systems,showing higher scores in important areas like accuracy,precision,recall,and F1-score,based on data from CICIoT 2023,UNSW-NB 15,and WUSTL-IIoT datasets.Results from the proposed framework show a 97.5%accuracy rate,indicating its potential as an effective solution for SCADA-IIoT cybersecurity against emerging threats.The research confirms that the proposed security and resilience methods are successful in protecting vital industrial control systems within their operational environments.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.
基金supported by the Natural Science Foundation of Anhui Province(Grant Number 2208085MG181)the Science Research Project of Higher Education Institutions in Anhui Province,Philosophy and Social Sciences(Grant Number 2023AH051063)the Open Fund of Key Laboratory of Anhui Higher Education Institutes(Grant Number CS2021-ZD01).
文摘The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.
