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Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization 认领 引用 被引量：1
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作者 WU Jin SU Zhengdong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期375-389,共15页
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ... Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method. 展开更多
关键词 wireless sensor network(WSN) swarm intelligence optimization artificial rabbits optimization(ARO) particle swarm optimization(PSO) coverage optimization
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Structural Optimization of a Multi-Story Frame Structure Based on a Pre-Trained Physics-Informed Neural Network(PINN)Surrogate Model 认领 引用
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作者 An Xu Zhixiong Liu +4 位作者 Hua Rong Liang Han Wei Shi Jun Huang Jiyang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期279-313,共35页
In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key struc... In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key structural characteristics and are limited to predicting global responses(e.g.,top displacement),but usually fail to achieve accurate internal force predictions with conventional training data volumes.As a result,most existing studies involving surrogate models did not concern internal force constraints.To address this issue,this study proposes a structural optimization framework based on a pre-trained Physics-Informed Neural Network(PINN)surrogate model.By embedding static equilibrium equation into the loss function,the model achieves higher predictive accuracy,particularly for internal forces,while pre-training accelerates convergence and enhances stability.Combined with an improved multi-swarm particle swarm optimization(MPSO)algorithm,the framework enables efficient optimization of multi-story frame structures under internal force and multiple other constraints.The application to a six-story frame structure validates its effectiveness:compared with a DNN-based model,the PINN-based model improves the coefficient of determination for internal force prediction from 0.8874 to 0.9937.These results demonstrate that the proposed method offers a promising approach for efficient optimization of multi-story frame structures. 展开更多
关键词 Structural optimization intelligent algorithms physics-informed neural networks(PINN) neural networks
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FedGNN:Federated Graph Neural Networks for Privacy-Preserving Cyber-Resilient Energy Optimization in IoT-Based Smart Grids 认领 引用
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作者 Alanoud Al Mazroa Fahad Masood +3 位作者 Bakri Hussain Awaji Mohammad Alhefdi Abeer Aljohani Jawad Ahmad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1462-1483,共22页
The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and priv... The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and privacy risks,as increased connectivity and data exchange can significantly disrupt energy management and system stability.Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks.This study proposes a privacy-preserving and cyber-resilient energy-optimization framework,FedGNN,for IoT-enabled smart grids that jointly integrates federated learning,graph neural network-based trust inference,and trust-aware energy dispatch.The framework dynamically learns node-level trust scores from multifeature measurements,including load,voltage,frequency,renewable generation,and battery storage,and incorporates them into real-time energy optimization.Results demonstrate that the proposed approach improves system resilience up to 12%,mitigates the impact of compromised nodes,and maintains operational reliability,while preserving the privacy of distributed data.A comparative analysis with baseline methods shows the proposed framework's superior performance in energy deviation,resilience,and trust-aware decision-making.The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids. 展开更多
关键词 Cyber security energy optimization graph neural networks IoT smart grids
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Optimization of Flying Ad Hoc Network Topology and Collaborative Path Planning for Multiple UAVs 认领 引用
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作者 Ming He Peizhao Wang +2 位作者 Haihua Chen Bin Sun Hongpeng Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1339-1352,共14页
Multiple unmanned aerial vehicles(UAVs)play a vital role in monitoring and data collection in wide area environments with harsh conditions.In most scenarios,issues such as real-time data retrieval and real-time UAV po... Multiple unmanned aerial vehicles(UAVs)play a vital role in monitoring and data collection in wide area environments with harsh conditions.In most scenarios,issues such as real-time data retrieval and real-time UAV positioning are often disregarded,essentially neglecting the communication constraints.In this paper,we comprehensively address both the coverage of the target area and the data transmission capabilities of the flying ad hoc network(FANET).The data throughput of the network is therefore maximized by optimizing the network topology and UAV trajectories.The resultant optimization problem is effectively solved by the proposed reinforcement learning-based trajectory planning(RL-TP)algorithm and the convex-based topology optimization(C-TOP)algorithm sequentially.The RL-TP optimizes the UAV paths while considering the constraints of FANET.The C-TOP maximizes the data throughput of the network while simultaneously constraining the neighbors and transmit powers of the UAVs,which is shown to be a convex problem that can be efficiently solved in polynomial time.Simulations and field experimental results show that the proposed optimization strategy can effectively plan the UAV trajectories and significantly improve the data throughput of the FANET over the adaptive local minimum spanning tree(A-LMST)and cyclic pruning-assisted power optimization(CPAPO)methods. 展开更多
关键词 Convex optimization flying ad hoc network (FANET) path planning reinforcement learning (RL) topology control
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Entropy-MIMR-LSTM framework for rain gauge network optimization in mountainous small watersheds:A case study of Fuhuxi Watershed,China 认领 引用
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作者 CUI Yulong WU Hao +3 位作者 ZHENG Jun MA Xianchun HUANG Yongwei LI Qiang 《Journal of Mountain Science》 SCIE CSCD 2026年第5期2210-2227,共18页
Global climate change has intensified the frequency and severity of extreme rainfall events,thereby exacerbating flood disasters.To mitigate such risks,timely and accurate rainfall measurements are essential,yet cost-... Global climate change has intensified the frequency and severity of extreme rainfall events,thereby exacerbating flood disasters.To mitigate such risks,timely and accurate rainfall measurements are essential,yet cost-effectiveness must also be considered.However,many river basins—particularly mountainous small watersheds—suffer from poorly designed rain gauge networks,limiting real-time data acquisition.Existing optimization methods are largely developed for large river basins or plains and are not directly applicable to mountainous small watersheds,where rainfall exhibits strong spatial heterogeneity and gauge networks are sparse.To address this gap,this study takes the Fuhuxi Watershed of Mount Emei in Sichuan Province,Southwest China,as a case study and develops a collaborative optimization framework integrating information entropy,the Maximum Information Minimum Redundancy(MIMR)criterion,and Long Short-Term Memory(LSTM)networks.Specifically,we quantified the information entropy matrix of seven existing rain gauge stations and applied the MIMR criterion,resulting in the retention of five key stations.The optimized network preserves 99%of the effective rainfall information from the original seven stations while significantly reducing operational and maintenance costs.Using data from nine rainfall-induced flood events between 2018 and 2023,we developed an LSTM-based runoff simulation model.The optimized network,which removes stations with low information content and high redundancy,achieved excellent flood simulation accuracy.The study demonstrates that:(1)information entropy theory effectively interprets the spatial correlation and information redundancy of rain gauge stations in mountainous small watersheds;and(2)the LSTM model validates the feasibility of using an optimized rain gauge network to support highprecision flood simulations.Finally,we propose suggestions for future research,particularly regarding the optimization of rain gauge networks to improve the understanding of optimal network design and thereby enhance the accuracy of rainfall-runoff simulations. 展开更多
关键词 Mountainous small watershed Rain gauge network optimization Information entropy MIMR criterion LSTM Flood simulation
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Dynamic Neural Networks for Manipulability Optimization of Omnidirectional Mobile Redundant Manipulator Under Anti-Input Disturbance 认领 引用
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作者 Shijun Tang Zhongbo Sun +1 位作者 Yunfeng Hu Hong Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1409-1427,共19页
Manipulability optimization plays a crucial role in the motion control of omnidirectional mobile redundant manipulators(OMRM),effectively reducing the risk of singularity.However,existing methods often overlook obstac... Manipulability optimization plays a crucial role in the motion control of omnidirectional mobile redundant manipulators(OMRM),effectively reducing the risk of singularity.However,existing methods often overlook obstacle avoidance or simplify obstacles as single points,limiting their practical applicability.To address these issues,this paper proposes a convex manipulability optimization scheme with physical and face-avoidance constraints(C-MOPOFC),where position tracking and matrix inversion are formulated as equality constraints,while physical limitations and obstacle avoidance are incorporated as inequality constraints.To enable real-time optimization,a resistant input disturbance recursive neural network(RID-RNN)is proposed,which solves the C-MOPOFC problem in an inverse-free manner,ensuring both real-time performance and robustness against disturbances.Additionally,it overcomes the limitations of traditional time-varying optimization solvers,which suffer from high computational complexity and weak disturbance suppression.Theoretical analysis proves that RID-RNN globally converges to the optimal solution of C-MOPOFC,even in the presence of noise.Finally,numerical simulations and physical experiments validate the proposed method,demonstrating its effectiveness in enhancing manipulability while ensuring safe operation in dynamic environments. 展开更多
关键词 Anti-input disturbance manipulability optimization omnidirectional mobile redundant manipulator (OMRM) recursive neural network
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Bi-Objective Optimization of Distribution Network Reliability Enhancement Using Quantitative Decomposition 认领 引用
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作者 Chenying Yi Yangjun Zhou +8 位作者 Wei Zhang Like Gao Hongwen Wu Yuanchao Zhou Ke Zhou Weixiang Huang Juntao Pan Shan Li Bin Feng 《Energy Engineering》 EI 2026年第8期262-276,共15页
Ensuring reliability in distribution networks is essential under increasing operational and economic constraints.Traditional planning models rely on power flow calculations,leading to high computational costs and poor... Ensuring reliability in distribution networks is essential under increasing operational and economic constraints.Traditional planning models rely on power flow calculations,leading to high computational costs and poor scalability.This study proposes a quantitative decomposition framework that establishes a direct linkage among reliability improvement measures,reliability parameters,and reliability indices,enabling fast and analytical reliability evaluation without power flow analysis.A bi-objective optimization model is developed to minimize both reliability indices(SAIDI)and investment costs,solved using Pareto-based multi-objective PSO combined with the TOPSIS method.Case studies on a 519-node distribution network demonstrate that the proposed approach achieves significant reliability improvement with superior computational efficiency,offering a practical and scalable tool for reliabilityoriented distribution planning. 展开更多
关键词 Distribution network reliability improvement measures quantitative analysis optimization decision
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Fixed-Time and Predefined-Time Synchronization of Inertial Memristive Neural Networks With Proportional Delay and Mismatched Switching Jumps via Stochastic Particle Swarm Optimization 认领 引用
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作者 Qiang Lai Jun Wang +1 位作者 Leimin Wang Minghong Qin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1274-1287,共14页
This paper studies the fixed-time synchronization(FxTS)and predefined-time synchronization(PTS)of a class of inertial memristive neural networks(IMNNs),which have unbounded proportional delay independent of time linea... This paper studies the fixed-time synchronization(FxTS)and predefined-time synchronization(PTS)of a class of inertial memristive neural networks(IMNNs),which have unbounded proportional delay independent of time linearity and mismatched switching jump coefficients.Based on Filippov solution theory and Lyapunov methods,this work develops an enhanced FxTS criterion delivering a tighter upper bound on convergence time and thereby extending guaranteed fixed-time behavior to a wider range of networks.A refined PTS condition that incorporates additional state-dependent terms accelerates error decay and reduces conservatism during the transient response.Numerical simulations show that the convergence rate of PTS under this strategy is significantly improved.Moreover,an optimization model with minimum control energy and dynamic error as objective functions is proposed to obtain more accurate controller parameters,and the stochastic inertia weight particle swarm optimization(SIWPSO)algorithm is introduced to solve the optimization model.Numerical studies not only validate the theoretical results for both FxTS and PTS but also demonstrate a secure communication application in which the chaos of the IMNNs acts as a masking carrier and enables perfect encryption and decryption of a complex test signal through SIWPSO-optimized FxTS and PTS. 展开更多
关键词 Fixed-time synchronization predefined-time synchronization inertial memristive neural networks proportional delay mismatched switching jumps stochastic particle swarm optimization
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Quantum-Inspired Optimization Algorithm for 3D Multi-Objective Base-Station Deployment in Next-Generation 5G/6G Wireless Network 认领 引用
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作者 Yao-Hsin Chou Cheng-Yen Hua +1 位作者 Ru-Wei Tseng Shu-Yu Kuo 《Computers, Materials & Continua》 SCIE EI 2026年第5期981-996,共16页
The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)w... The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios. 展开更多
关键词 3D network deployment quantum-inspired optimization B5G/6G multi-objective optimization coverage deployment cost urban wireless planning
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NPO-BAND:Number and placement optimization for backhaul-aware UAV network deployment 认领 引用
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作者 Sining Yang Bo Zhang Jinshu Su 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期630-639,共10页
In emergency communication scenarios,exploiting Unmanned Aerial Vehicles(UAVs)as relays to provide wireless communication services for ground users has emerged as a promising application.A key challenge in this resour... In emergency communication scenarios,exploiting Unmanned Aerial Vehicles(UAVs)as relays to provide wireless communication services for ground users has emerged as a promising application.A key challenge in this resource-constrained application is deploying the minimum number of UAVs to form an aerial backhaul network to ensure coverage,which composes the Number and Placement Optimization for the Backhaul-Aware Network Deployment(NPO-BAND)problem.In this paper,we first formulate the NPO-BAND problem based on the geometric disk coverage model.Then,we propose a low-complexity heuristic method to solve this NP-hard problem.The proposed method contains a Very Important Point-Choosing(VIPC)strategy and a Backhaul-Aware Local Coverage(BALC)algorithm.Specifically,the VIPC strategy weighs up the backhaul connectivity constraint and the ground user coverage to choose the VIP,while the BALC algorithm solves the extended 1-center problem to determine the deployment location of each UAV.Simulation results show that the proposed method can effectively reduce the number of deployed UAVs,saving up to 25%-50%of that compared to existing methods across varying numbers and area sizes in clustered distribution patterns of ground users. 展开更多
关键词 Unmanned aerial vehicles Multi-UAV network Backhaul-aware deployment Number optimization
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Improved Whale Optimization-Based Neural Network Predictive Control for Industrial Refrigeration Systems 认领 引用
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作者 Qi Li Menghan Yang +1 位作者 Shifa Cui Kun Han 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第3期1-14,共14页
The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such ... The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such systems a significant and challenging research problem.To address this,a novel Neural Network Predictive Control(NNPC)algorithm is proposed,which integrates an Improved Deep Belief Network(IDBN)with an Improved Whale Optimization Algorithm(IWOA).First,the IDBN acts as a high-precision nonlinear prediction model,significantly improving multi-step prediction accuracy.Second,the IWOA is employed to optimize the predictive controller,featuring three major improvements:an improved population initialization,a modified convergence factor update mechanism,and an added disturbance strategy,which collectively accelerate convergence and enhance global search capability.Finally,simulation results demonstrate that the proposed NNPC algorithm achieves superior set-point tracking performance and strong robustness against external disturbances. 展开更多
关键词 industrial refrigeration systems model predictive control time⁃delay whale optimization algorithm deep belief network
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Reconstruction of fields based on physics-informed neural networks with sensor placement optimization 认领 引用 被引量：1
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作者 Bingteng Sun Shengze Cai +4 位作者 Qiang Du Zinan Wang Yiling Chen Zhen Tian Junqiang Zhu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期150-162,共13页
High-fidelity field reconstruction has been a focal point for many research studies,as the measured sensor data are often sparse and incomplete in both time and space.Physics-informed neural networks(PINNs)have been p... High-fidelity field reconstruction has been a focal point for many research studies,as the measured sensor data are often sparse and incomplete in both time and space.Physics-informed neural networks(PINNs)have been proposed to reconstruct fields using imperfect data,as they incorporate physical principles and thereby reduce reliance on the known sensor data.However,the placement of sensors remains crucial for optimizing PINNs,and existing studies have not sufficiently considered this aspect.Therefore,developing algorithms that intelligently improve sensor placement is of significant importance.In this study,we introduce a general approach that employs differentiable programming with attention modules to optimize sensor placement during the training of a PINNs model in order to improve field reconstruction.We evaluate our method using three distinct cases:the Allen-Cahn equation problem,the lid-driven cavity flow problem,and the cylinder flow problem to demonstrate our approach effectiveness in flow field inference,system identification,and its capability for multi-condition generalization.The results indicate that our method improves test scores and effectively learns the optimal layout of sensors for various Reynolds numbers,which advances our understanding of the relationship between sensor placement and reconstruction precision using PINNs. 展开更多
关键词 Physics-informed neural network Sensor placement optimization Field reconstruction
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Dual Layer Source Grid Load Storage Collaborative Planning Model Based on Benders Decomposition: Distribution Network Optimization Considering Low-Carbon and Economy 认领 引用 被引量：1
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作者 Jun Guo Maoyuan Chen +2 位作者 Yuyang Li Sibo Feng Guangyu Fu 《Energy Engineering》 EI 2026年第2期104-133,共30页
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ... Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability. 展开更多
关键词 Benders decomposition source grid load storage distribution network planning low-carbon economy optimization model
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Solving high-dimensional global optimization problems via solution space restructuring with neural network 认领 引用
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作者 N.VO T.LE-DUC +3 位作者 H.TANG H.NGUYEN-XUAN S.H.LEE J.H.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第6期1383-1400,共18页
It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-fre... It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-free techniques.However,the general framework for adaptively dealing with a particular optimization problem is commonly overlooked and thus still hidden in the literature.To overcome this limitation,a new approach assisted by the neural network(NN)is proposed for solving high-dimensional optimization issues.By restructuring the search space to optimize the objective function via a nonlinear mapping constructed by an autoencoder(AE),the surrogate solution space is constructed by a network training process and dynamically oriented to the optimal solution of the optimization issue.To enhance the optimization efficiency and address non-smooth problems,the classical metaheuristic grey wolf optimizer(GWO)and the adaptive moment estimation(Adam)are sequentially employed to complement the disadvantages of the constituted models.The effectiveness of the proposed approach is validated by solving a set of mathematical functions with 1000-dimensional and three large-scale truss design optimization problems.Several numerical experiments show that the solution space is reduced in terms of both size and complexity based on the restructuring procedure,in which the global optimal solution is still conserved,leading to better optimization efficiency when solving optimization problems with complex search domains with large dimensions.In addition,the hybrid optimizer has also been proven to be more effective when combined with the restructuring technique owing to the use of the Adam algorithm in the second phase. 展开更多
关键词 high-dimensional optimization solution space restructuring adaptive moment estimation(Adam) grey wolf optimizer(GWO) neural network(NN)
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Tropical cyclone intensity prediction based on Kolmogorov–Arnold networks with predictor pruning optimization 认领 引用
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作者 Keyun Li Wei Zhong +3 位作者 Yao Yao Fangzhao Li Yuan Sun Hongrang He 《Atmospheric and Oceanic Science Letters》 CAS CSCD 2026年第4期47-52,共6页
Accurate tropical cyclone(TC)intensity prediction is crucial for disaster mitigation and public safety.However,current TC intensity prediction models are facing significant challenges,including intricate predictor sel... Accurate tropical cyclone(TC)intensity prediction is crucial for disaster mitigation and public safety.However,current TC intensity prediction models are facing significant challenges,including intricate predictor selection and limited accuracy.To address these challenges,the authors propose a novel TC intensity prediction framework that integrates Kolmogorov–Arnold networks(KANs)with a dynamic predictor pruning optimization module—namely,TCI–KAN.This model develops a data-driven predictor selection method that implements iterative elimination of low-impact predictors through weight ranking analysis.Testing results demonstrate that TCI–KAN achieves superior accuracy in 6-h intensity forecasts,with a mean absolute error(MAE)of 2.85 kt.TCI–KAN significantly outperforms the referenced best records by 31%,13%,and 6%in MAE compared to the official operational forecast,single deep learning models,and hybrid deep learning models,respectively.Further analysis demonstrates that TCI–KAN is suitable for different basins and TC categories. 展开更多
关键词 Tropical cyclone Kolmogorov–Arnold networks Predictor pruning optimization Intensity prediction
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A Bilevel Deep Learning Optimization Framework for Joint Energy Harvesting Prediction and Energy-Aware Scheduling in IoT-Based Wireless Sensor Networks 认领 引用
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作者 Mohammad Q.Al-Jamal Mahmoud Al Jamal +3 位作者 Bashar S.Khassawneh Ayoub Alsarhan Amina Salhi Tahani Alsubait 《Computers, Materials & Continua》 SCIE EI 2026年第9期917-944,共28页
Energy sustainability and secure operation are persistent challenges in Internet-of-Things(IoT)wireless sensor networks(WSNs),where limited battery capacity,heterogeneous traffic,and security procedures jointly drive ... Energy sustainability and secure operation are persistent challenges in Internet-of-Things(IoT)wireless sensor networks(WSNs),where limited battery capacity,heterogeneous traffic,and security procedures jointly drive premature node depletion and service degradation.This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust,energy-aware scheduling for clustered IoT-WSNs.At the lower level,a lightweight temporal predictor(TCN+LSTM with stochastic sampling)learns short-horizon residual-energy evolution from multivariate,dataset-aligned windows capturing sensing/communication activity,proximity-to-cluster-head effects,and security overhead(authentication latency,key exchange,and rekeying),and produces both point forecasts and uncertainty estimates to enable risk-sensitive control.At the upper level,a constrained,horizon-based scheduler selects per-node actions(duty cycle,sensing rate,transmission power)to extend network lifetime and balance residual energy while enforcing safety thresholds and operational bounds;bilevel coupling is realized via differentiable hypergradient updates,complemented by trust-region action smoothing and adaptive primal–dual constraint handling to suppress energy-critical states under uncertainty.On a real-world WSN energy–security dataset,the proposed model attains the best lower-level learning performance with MAE=0.004,RMSE=0.006,and R²=0.995 for residual-energy regression,and up to 0.98 accuracy/0.98 F1 for secure-and-efficient classification.End-to-end scheduling results show that the full framework improves estimated network lifetime by up to 1.60×,reduces residual-energy variance to 0.60×,and lowers safety violations to 0.35×relative to a fixed-policy baseline,demonstrating robust,secure,and sustainable IoT-enabled WSN operation. 展开更多
关键词 Internet of Things wireless sensor networks bilevel optimization energy-aware scheduling
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Review of the analysis and methods of natural gas pipeline network operation optimization 认领 引用
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作者 Deming Zhao Wuchang Wang Yuxing Li 《Natural Gas Industry B》 2026年第3期403-424,共22页
The expansion of natural gas consumption and pipeline construction makes integrating artificial intelligence into pipeline network operations increasingly essential.This review summarizes progress in operation optimiz... The expansion of natural gas consumption and pipeline construction makes integrating artificial intelligence into pipeline network operations increasingly essential.This review summarizes progress in operation optimization,gas transmission capacity evaluation,and solution algorithms.The review systematically summarizes objective functions,hydraulichermal and compressor constraints,and decision variables,all framed by operator objectives such as transmission capacity,economic benefits,and supply reliability.It highlights gas transmission capacity optimization and extended models,including those for hydrogen-blended and renewable energy-coupled scenarios.The review also analyzes applications of deterministic and stochastic intelligent algorithms,alongside deep learning and hyper-heuristic methods.Key findings indicate that:(1)Traditional models often lack safety,reliability,and low-carbon indicators;(2)Deterministic algorithms struggle with high dimensionality,while heuristic algorithms are prone to premature convergence;(3)Hydrogen blending and new energy integration necessitate revised constraints;and(4)Existing online dynamic optimization methods are insufficient.Finally,current shortcomings are identified,and future directions,such as advanced online dynamic optimization and cross-domain intelligence,are proposed.In conclusion,while artificial intelligence is crucial for natural gas pipeline network operations,significant limitations persist.Future research must prioritize addressing these gaps to advance the industry's intelligent,low-carbon,and reliable development. 展开更多
关键词 Natural gas pipeline network Operation optimization Optimization algorithm Gas transmission capacity evaluation
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A Novel Binary Classification Neural Network Optimized by the Mosquito Mating Swarm Optimization Algorithm for Predicting Microgrid Operational Modes 认领 引用
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作者 Jesús Aguila-León Carlos Vargas-Salgado +1 位作者 Dácil Díaz-Bello Fabián Lara-Vargas 《Energy Engineering》 EI 2026年第7期150-172,共23页
Integrating renewable energy sources presents technical challenges due to their variable nature,particularly in predicting and managing microgrid operational modes.Accurate identification of grid statesinterconnected ... Integrating renewable energy sources presents technical challenges due to their variable nature,particularly in predicting and managing microgrid operational modes.Accurate identification of grid statesinterconnected or islanded—is essential for maintaining stability and optimizing performance under fluctuating environmental conditions to meet energy demand.This work proposes a bio-inspired,optimized binary classification model based on Multi-Layer Perceptron Artificial Neural Networks(MLP-ANN),with the architecture and hyperparameters tuned using the novel Mosquito Mating Swarm Optimization(MMSO)algorithm,inspired by mosquito mating behavior and swarm dynamics.The model employs an MLP-ANN with a variable number of hidden layers and neurons per layer,configured to maximize classification accuracy by dynamically adjusting parameters,including the learning rate and regularization coefficients.Training utilizes k-fold cross-validation on experimental microgrid data.The MMSO approach is benchmarked against Particle Swarm Optimization(PSO),Genetic Algorithm(GA),and Grey Wolf Optimizer(GWO)to validate its effectiveness.Results show that the MMSO-optimized MLP-ANN achieved an 86.34%recall,98.96%precision,and 92.29%accuracy,while minimizing the Mean Squared Error to 0.0206.The MMSO-optimized MLP-ANN model achieved competitive classification performance compared to the other algorithms evaluated;although no statistically significant differences in recall were observed among the optimizers(p=0.22),the MMSO achieved the lowest MSE(0.0206).The MMSO was the only algorithm capable of discovering a four-layer architecture hidden within the same search space,evidencing superior exploration of deeper architectural regions of the solution space.These findings demonstrate the model's capacity to predict microgrid operational modes under variable conditions,highlighting the potential of integrating bio-inspired algorithms with neural networks for energy management systems.This approach could enhance the efficiency and reliability of integrating renewable energy sources into dynamic energy systems. 展开更多
关键词 Microgrids binary classification deep learning artificial neural network mosquito mating swarm optimizer operational mode prediction
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Comparative Analysis of Genetic and Quantum-Inspired Optimization for Zero-Trust Microsegmentation in Brownfield Networks 认领 引用
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作者 Reen-Cheng Wang Hong-Sheng Wang Kuo-Chun Tseng 《Computers, Materials & Continua》 SCIE EI 2026年第9期1407-1421,共15页
Network microsegmentation has become a key mechanism for enforcing zero-trust architecture in enterprise environments,yet its effectiveness remains closely tied to initialization quality.This study formulates network ... Network microsegmentation has become a key mechanism for enforcing zero-trust architecture in enterprise environments,yet its effectiveness remains closely tied to initialization quality.This study formulates network microsegmentation as a state-dependent combinatorial optimization problem in which optimization behavior depends on the availability of structural guidance.A comparative analysis is conducted across four representative optimization paradigms,including genetic algorithms(GA),differential evolution(DE),particle swarm optimization(PSO),and amplitude-ensemble quantum-inspired tabu search(AE-QTS),under both structured and unstructured conditions.Experiments are conducted on a representative brownfield enterprise network using 30 independent runs per configuration.In addition to cost-based evaluation,a fragmentation metric is used to assess the structural quality and manageability of segmentation outcomes.The results indicate that under structured conditions,GA and AE-QTS achieve the best overall performance,with AE-QTS obtaining the best average objective value of−61.29 and GA demonstrating rapid convergence under limited optimization time.Under unstructured conditions,AE-QTS consistently outperforms all other methods,reducing the average objective value from 344.28(GA)and 1214.60(DE)to 4.54 under uniform initialization.Moreover,PSO demonstrates comparatively stable and robust behavior,although its performance remains below that of AE-QTS.These findings suggest that microsegmentation can be more appropriately viewed as a condition-dependent optimization problem,in which different optimization methods exhibit different strengths across operational scenarios.The results provide empirical evidence and practical insights that may support the future development of adaptive or hybrid optimization strategies for real-world deployment environments. 展开更多
关键词 Network microsegmentation zero-trust architecture quantum-inspired algorithm genetic algorithm state-dependent optimization
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Multi-Objective and Multi-Criteria Optimization of Energy Storage Planning in Renewable Distribution Networks 认领 引用
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作者 Alireza Norouzpour Shahrbejari Nafiseh Pishbin +2 位作者 Mohammad Reza Maghami Mazlan Mohamed Mohammad Golmohammad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期581-619,共39页
This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting,sizing,and scenario-based operation of energy storage systems(ESSs)in renewable-integrated distribution network... This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting,sizing,and scenario-based operation of energy storage systems(ESSs)in renewable-integrated distribution networks.The proposed model concurrently addresses technical,economic,and reliability objectives—minimizing active power losses(PL),voltage deviation(VD),expected energy not supplied(EENS),and short-circuit level(SCL),while maximizing voltage sensitivity index(VSI)and power-loss sensitivity factor(PLSF).A Particle Swarm Optimization(PSO)algorithm with weighted-sum scalarization is employed to solve this complex,nonlinear optimization problem and effectively balance the conflicting operational goals.The framework is validated using IEEE 69-bus and IEEE 118-bus test systems under varying load conditions(20%,50%,100%,and 150%)with time-dependent photovoltaic(PV)and wind turbine(WT)generation profiles.Results demonstrate that the proposed approach achieves significant performance enhancements,reducing power losses by up to 54%,EENS by 88%,and operational cost by 22%while maintaining SCL values within protection limits.Furthermore,the inclusion of ESS units improves system reliability and voltage stability,ensuring smooth operation during load fluctuations and fault conditions.The findings confirm that the proposed weighted-sum multi-criteria optimization framework using PSO provides a scalable and protection-aware solution for integrating ESSs into renewable-rich distribution networks.It offers a robust planning and operational tool for next-generation smart grids,enabling a more efficient,resilient,and sustainable energy ecosystem. 展开更多
关键词 Energy storage systems multi-objective optimization optimal placements distribution networks renewable integration grid reliability这篇论文的主要研究内容是什么?这篇论文的作者单位都有哪些?这篇论文的研究成果有哪些应用?
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