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
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
This study develops a GWO-optimized cascaded fuzzy-PID controller with triangular membership functions for load frequency control in interconnected power systems.The controller’s effectiveness is demonstrated on ther...This study develops a GWO-optimized cascaded fuzzy-PID controller with triangular membership functions for load frequency control in interconnected power systems.The controller’s effectiveness is demonstrated on thermal–thermal and hybrid thermal–hydro–gas power systems.The controller parameters were tuned using the Integral Time Absolute Error(ITAE)objective function,which was also evaluated alongside other objective functions(IAE,ISE,and ITSE)to ensure high precision in frequency stabilization.To validate the effectiveness of the triangular membership function,comparisons were made with fuzzy-PID controllers employing trapezoidal and Gaussian membership functions.Performance metrics,including ITAE,settling time,overshoot,and undershoot of frequency deviation,as well as tie-line power deviation,were evaluated.Robustness was established through a comprehensive sensitivity analysis with T_(G),T_(T),andT_(R) parameter variations(±50%),a non-linearity analysis incorporating Generation Rate Constraint(GRC)and Governor Deadband(GDB),a random Step Load Perturbation(SLP)over 0–100 s,and also Stability analysis of the proposed scheme is conducted using multiple approaches,including frequency-domain analysis,Lyapunov stability theory,and eigenvalue analysis.Additionally,the system incorporating thermal,hydro,and gas turbines,along with advanced components like CES and HVDC links,was analysed.Comparisons were conducted against controllers optimized using Modified Grasshopper Optimization Algorithm(MGOA),Honey Badger Algorithm(HBA),Particle Swarm Optimization(PSO),Artificial Bee Colony(ABC),and Spider Monkey Optimization(SMO)algorithms.Results demonstrate that the GWO-based fuzzy-PID controller outperforms the alternatives,exhibiting superior performance across all evaluated metrics.This highlights the potential of the proposed approach as a robust solution for load frequency control in complex and dynamic power systems.展开更多
Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,t...Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats,potentially leading to operational failures and data breaches.Furthermore,CPS faces significant threats related to unauthorized access,improper management,and tampering of the content it generates.In this paper,we propose an intrusion detection system(IDS)optimized for CPS environments using a hybrid approach by combining a natureinspired feature selection scheme,such as Grey Wolf Optimization(GWO),in connection with the emerging Light Gradient Boosting Machine(LightGBM)classifier,named as GWO-LightGBM.While gradient boosting methods have been explored in prior IDS research,our novelty lies in proposing a hybrid approach targeting CPS-specific operational constraints,such as low-latency response and accurate detection of rare and critical attack types.We evaluate GWO-LightGBM against GWO-XGBoost,GWO-CatBoost,and an artificial neural network(ANN)baseline using the NSL-KDD and CIC-IDS-2017 benchmark datasets.The proposed models are assessed across multiple metrics,including accuracy,precision,recall,and F1-score,with an emphasis on class-wise performance and training efficiency.The proposed GWO-LightGBM model achieves the highest overall accuracy(99.73%)for NSL-KDD and(99.61%)for CIC-IDS-2017,demonstrating superior performance in detecting minority classes such as Remote-to-Local(R2L)and Other attacks—commonly overlooked by other classifiers.Moreover,the proposed model consumes lower training time,highlighting its practical feasibility and scalability for real-time CPS deployment.展开更多
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This study develops a GWO-optimized cascaded fuzzy-PID controller with triangular membership functions for load frequency control in interconnected power systems.The controller’s effectiveness is demonstrated on thermal–thermal and hybrid thermal–hydro–gas power systems.The controller parameters were tuned using the Integral Time Absolute Error(ITAE)objective function,which was also evaluated alongside other objective functions(IAE,ISE,and ITSE)to ensure high precision in frequency stabilization.To validate the effectiveness of the triangular membership function,comparisons were made with fuzzy-PID controllers employing trapezoidal and Gaussian membership functions.Performance metrics,including ITAE,settling time,overshoot,and undershoot of frequency deviation,as well as tie-line power deviation,were evaluated.Robustness was established through a comprehensive sensitivity analysis with T_(G),T_(T),andT_(R) parameter variations(±50%),a non-linearity analysis incorporating Generation Rate Constraint(GRC)and Governor Deadband(GDB),a random Step Load Perturbation(SLP)over 0–100 s,and also Stability analysis of the proposed scheme is conducted using multiple approaches,including frequency-domain analysis,Lyapunov stability theory,and eigenvalue analysis.Additionally,the system incorporating thermal,hydro,and gas turbines,along with advanced components like CES and HVDC links,was analysed.Comparisons were conducted against controllers optimized using Modified Grasshopper Optimization Algorithm(MGOA),Honey Badger Algorithm(HBA),Particle Swarm Optimization(PSO),Artificial Bee Colony(ABC),and Spider Monkey Optimization(SMO)algorithms.Results demonstrate that the GWO-based fuzzy-PID controller outperforms the alternatives,exhibiting superior performance across all evaluated metrics.This highlights the potential of the proposed approach as a robust solution for load frequency control in complex and dynamic power systems.
基金supported by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats,potentially leading to operational failures and data breaches.Furthermore,CPS faces significant threats related to unauthorized access,improper management,and tampering of the content it generates.In this paper,we propose an intrusion detection system(IDS)optimized for CPS environments using a hybrid approach by combining a natureinspired feature selection scheme,such as Grey Wolf Optimization(GWO),in connection with the emerging Light Gradient Boosting Machine(LightGBM)classifier,named as GWO-LightGBM.While gradient boosting methods have been explored in prior IDS research,our novelty lies in proposing a hybrid approach targeting CPS-specific operational constraints,such as low-latency response and accurate detection of rare and critical attack types.We evaluate GWO-LightGBM against GWO-XGBoost,GWO-CatBoost,and an artificial neural network(ANN)baseline using the NSL-KDD and CIC-IDS-2017 benchmark datasets.The proposed models are assessed across multiple metrics,including accuracy,precision,recall,and F1-score,with an emphasis on class-wise performance and training efficiency.The proposed GWO-LightGBM model achieves the highest overall accuracy(99.73%)for NSL-KDD and(99.61%)for CIC-IDS-2017,demonstrating superior performance in detecting minority classes such as Remote-to-Local(R2L)and Other attacks—commonly overlooked by other classifiers.Moreover,the proposed model consumes lower training time,highlighting its practical feasibility and scalability for real-time CPS deployment.
