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Adaptive elite ant colony optimization for track planning in gravity-aided navigation with multi-feature fusion 认领 引用
1
作者 Jinbai Zhang Hao Zhou +4 位作者 Yun Xiao Xinshang Li Hong Li Yifeng Chen Zhicai Luo 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期112-126,共15页
Autonomous Underwater Vehicle track planning is critical for maritime defense missions,particularly in signal-denied and stealth-sensitive environments.Gravity-aided inertial navigation systems(GAINS),as a passive and... Autonomous Underwater Vehicle track planning is critical for maritime defense missions,particularly in signal-denied and stealth-sensitive environments.Gravity-aided inertial navigation systems(GAINS),as a passive and emission-free approach,offer strong potential for such missions.However,track planning under gravity constraints remains underexplored.This paper proposes an Adaptive Elite Ant Colony Optimization(AEACO)algorithm to address this gap.AEACO integrates two key strategies:an elite reinforcement mechanism inspired by genetic algorithms and a dynamic parameter adjustment method for pheromone-related variables.A gravity adaptability model is first established using fuzzy statistics and entropy-weighted feature fusion to identify navigable regions.AEACO then reinforces elite path segments and self-adjusts its parameters in response to gravity field variations.Experiments across 22real-world marine gravity scenarios show that AEACO consistently outperforms various classical methods.Specifically,it achieves up to 19%shorter paths,40%fewer turns,and 95%faster convergence.Unlike other Ant Colony Optimization(ACO)variants,AEACO operates without fixed parameters or external tuning,making it scalable and adaptable for real-time defense operations in complex underwater environments. 展开更多
关键词 Ant colony optimization Elite strategy Gravity-aided inertial navigation Track planning Gravity adaptive regions
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Aerodynamic/control coupling optimization method for reusable vehicles using adaptive partitioning 认领 引用
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作者 Lulu JIANG Xin PAN +2 位作者 Chao DONG Yuan FU Gang CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期340-354,共15页
To address the issues of poor lateral stability,strong interactions between channels,and the inherent trade-offs of global optimization methods in the aerodynamic shape design of reusable hypersonic vehicles,this stud... To address the issues of poor lateral stability,strong interactions between channels,and the inherent trade-offs of global optimization methods in the aerodynamic shape design of reusable hypersonic vehicles,this study proposes an Adaptive Partitioning Optimization(APO)method for aerodynamic/control coupling design.The APO method explicitly integrates the interactions between aerodynamic configuration and longitudinal/lateral control performance,while introducing a variable correlation-based partitioning strategy.This enables multi-channel aerodynamic/control collaborative optimization while avoiding the performance compromises associated with global multidisciplinary optimization.To address the high computational cost of control performance evaluation,a sample augmentation strategy with interpolation correction is introduced,reducing cost while maintaining accuracy.Optimization of a representative reusable vehicle demonstrates that this framework achieves a 2.23%increase in lift-to-drag ratio,a 0.56%reduction in drag coefficient,and enhanced lateral stability.Moreover,it achieves better coordination between aerodynamic and control objectives compared to global optimization.These results highlight the practical value of the APO method in improving aerodynamic and control performance for reusable hypersonic vehicles,offering a scalable and computationally efficient solution for multidisciplinary aerodynamic/control co-design in hypersonic vehicle applications. 展开更多
关键词 Adaptive partitioning Aerodynamic/control coupling Multichannel control Reusable vehicle Shape optimization Small sample interpolation
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Disturbance observer-based constrained adaptive fuzzy control for manipulator:a cooperative optimization approach of gain 认领 引用
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作者 Qing Yang Haisheng Yu +2 位作者 Xiangxiang Meng Wenqian Yu Qingkun Guo 《Control Theory and Technology》 EI CSCD 2026年第3期416-432,共17页
This paper investigates input–output constraints adaptive fuzzy control strategy with cooperative optimization approach of the gain and time-varying nonlinear disturbance observer for manipulator systems.First,the st... This paper investigates input–output constraints adaptive fuzzy control strategy with cooperative optimization approach of the gain and time-varying nonlinear disturbance observer for manipulator systems.First,the static control gain strategies cannot simultaneously optimize system performances during both dynamic and steady-state stages.To address this problem,a novel cooperative optimization approach of the gain(COG)based on tracking error is proposed to replace the traditional static gain strategies.Second,a time-varying nonlinear disturbance observer(NDO)is proposed to accurately estimate variable disturbances and mitigate harmful observation peak at the initial stage of manipulator tracking.Furthermore,an auxiliary system and an asymmetric time-varying barrier Lyapunov function are used to ensure that the inputs and outputs of the system remain within predefined constraints.Notably,the traditional backstepping control relies on precise model information.To minimize the impact of model uncertainties on tracking performance,an adaptive fuzzy control is employed to design the controller,eliminating the need for precise model information.Finally,the effectiveness of the proposed input–output constraints adaptive fuzzy control strategy with COG and time-varying NDO is verified and analyzed through comparative experiments on a two-joint manipulator platform. 展开更多
关键词 Cooperative optimization Adaptive fuzzy control Barrier Lyapunov function Disturbance observer Constrained manipulator
Multi-strategy improved sand cat swarm optimization based on somersault pursuit and adaptive Lévy flight 认领 引用
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作者 Wu Jin Xiong Hao +1 位作者 Luo Wenxuan Hao Chengbin 《High Technology Letters》 EI CAS 2026年第1期30-38,共9页
To address the limitations of the sand cat swarm optimization(SCSO) algorithm which are slow convergence and low accuracy in complex problems,this study proposes an improved SCSO(ISCSO) algorithm that integrates multi... To address the limitations of the sand cat swarm optimization(SCSO) algorithm which are slow convergence and low accuracy in complex problems,this study proposes an improved SCSO(ISCSO) algorithm that integrates multiple enhancement strategies.Firstly,Kent chaotic mapping initializes the population for uniform distribution.Secondly,somersault foraging strategy is introduced during the search and attack phases,allowing the algorithm to escape local optima by intercepting evasive prey.Simultaneously,an adaptive Lévy flight strategy is incorporated into the attack phase to bolster global exploration.Finally,the vertical and horizontal crossover strategy is implemented to enhance population diversity.The performance of the proposed algorithm is evaluated using 16 benchmark test functions.The experimental results demonstrate that ISCSO significantly outperforms the original SCSO and shows notable advantages over other metaheuristic algorithms.Furthermore,application to a pressure vessel design problem verifies ISCSO's effectiveness in solving practical engineering optimization challenges. 展开更多
关键词 sand cat swarm optimization Kent chaotic mapping somersault pursuit adaptive Lévy flight vertical and horizontal crossover
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Performance degradation and maintenance optimization strategy of rolling bearings based on data fusion and adaptive partition 认领 引用
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作者 Hong-Chuan Cheng Xin-Hai Li +3 位作者 Guo-Hui Ma Yu Cui Zhi-Wu Shang Xia-Fei Shi 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期178-194,共17页
The traditional method of performance degradation prediction and maintenance of rolling bearings only considers a single sensor signal,which makes it difficult to automatically partition degradation stages and prone t... The traditional method of performance degradation prediction and maintenance of rolling bearings only considers a single sensor signal,which makes it difficult to automatically partition degradation stages and prone to over-detection.A new method of performance degradation evaluation and maintenance of rolling bearings based on data-level fusion,adaptive health state partitioning,and state maintenance is proposed.Firstly,considering the degradation and impact in the process of bearing deterioration,the multi-sensor signals are dynamically weighted to achieve data-level fusion.Secondly,a bearing health index was established based on fast spectral correlation,Wasserstein distance,and linear rectification techniques.On this basis,by combining the Bayesian information criterion and the elbow rule,the precise division of rolling bearing health state is realized through hidden Markov model regression.Then,random forest was used to classify and predict the data to verify the validity of the proposed data fusion method and health indicator.Finally,condition-based maintenance strategy based on the fourth moment,stress-strength interference model,and Gamma process is proposed to avoid excessive detection and reduce maintenance costs.Through accelerated degradation experiments and field validation tests on the rolling bearing test data set of Xi’an Jiaotong University and FEMTO(PRONOSTIA),the accuracy and superiority of the proposed method in the prediction and maintenance of bearing health state are verified. 展开更多
关键词 Rolling bearing Data fusion Gamma process Adaptive partition Maintenance strategy
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Environment-adaptive design of multilayer optical structures for surface polariton excitation via dynamic multi-objective optimization 认领 引用
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作者 Ru Lei Lin Li +4 位作者 Yiqi Feng Ziyue Ren Kun Zhang Lixun Sun Ting Mei 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期267-279,共13页
The graphene±dielectric multilayer architecture constitutes a fundamental and widely utilized platform for sustaining surface polariton(SP)propagation.Owing to their extraordinary prospects in defence critical te... The graphene±dielectric multilayer architecture constitutes a fundamental and widely utilized platform for sustaining surface polariton(SP)propagation.Owing to their extraordinary prospects in defence critical technologies,including radar-absorbing stealth coatings,high-power microwave shielding,and ultrafast optical switching,SPs have attracted intense and sustained interest.In this study,we develop an environment-adaptive design framework that models wavelength variation as a dynamic environmental change and automatically adjusts the design parameters in response.Our method employs a dynamic multi-objective optimization algorithm augmented with a predictive transfer strategy,optimizing SP coupling efficiency,structural compactness,and fabrication feasibility.Using a population history prediction mechanism,the framework not only adaptively generates multilayer designs across the full visible spectrum without full re-initialization,but also retains and exploits knowledge of how environmental variations influence the distribution of optimal solutions.This enables rapid adjustment of the optimization direction when parameters such as wavelength,angle,or doping change,thus avoiding the need to restart the search from scratch.Comprehensive comparisons demonstrate outstanding robustness under continuous wavelength shifts.The optimized graphene-coated distributed Bragg reflector(DBR)stacks achieve near-perfect absorption(>98%)at each individual wavelength across the visible spectrum.This work not only provides theoretical guidance for SP excitation experiments,but also contributes to the optimization of polariton device design,which is crucial for enhancing the performance of defence-related optical systems. 展开更多
关键词 Distributed bragg reflectors Surface polariton Dynamic multi-objective optimization
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Enhanced accident diagnosis for molten salt reactors using an adaptive residual CNN with Bayesian optimization 认领 引用
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作者 Chao‑Qun Wang Kai Wang +6 位作者 Qun Yang Rong‑Jian Liang Xin Yue Liang‑Jie Sun Wen‑Tao Jiang Zhao‑Zhong He Na‑Xiu Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第8期280-296,共17页
Safety is of paramount importance in nuclear power plants.Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems.The convergence of Industry 4.0 technologies and deep ... Safety is of paramount importance in nuclear power plants.Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems.The convergence of Industry 4.0 technologies and deep learning methods has emerged as a promising approach for improving the operational safety of nuclear energy systems,particularly in fault detection and diagnosis(FDD)applications.This study proposes a novel adaptive accident diagnosis framework tailored for molten salt reactors(MSRs)based on an enhanced residual convolutional neural network(AM-RCNN).The AM-RCNN incorporates an anti-noise module implemented using the soft thresholding method,together with an attention mechanism,to improve robustness.Datasets representing eight distinct operational scenarios were generated using the RELAP5-TMSR simulation tool.An appropriate subset of input features for MSR accident diagnosis was selected using Pearson correlation analysis and random forest importance ranking.The models were subsequently trained,validated,optimized,and tested.Comparative analyses with conventional RCNN and CNN architectures demonstrate the diagnostic advantages of the proposed approach.In addition,the integration of Bayesian optimization further enhances the performance of the AM-RCNN.As a contribution to intelligent monitoring research for MSRs,the proposed method provides reliable decision support for nuclear system operation,particularly in autonomous scenarios. 展开更多
关键词 Accident diagnosis Feature selection Deep residual CNN Bayesian optimization Molten salt reactors
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Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
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作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
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Adaptive distributed optimization of high-order nonlinear multi-agent systems with predefined accuracy under state observer 认领 引用
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作者 Xiao-Wen Zhao Tong Shu +2 位作者 Deng-Hao Pang Tao Li Mei Yao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第3期236-247,共12页
This paper discusses adaptive distributed optimization with predefined accuracy for high-order nonlinear multi-agent systems(MASs)that are subject to disturbances and nonlinear uncertainties.To estimate the global opt... This paper discusses adaptive distributed optimization with predefined accuracy for high-order nonlinear multi-agent systems(MASs)that are subject to disturbances and nonlinear uncertainties.To estimate the global optimal solution in realtime,a distributed proportional-integral optimization technique is used to generate a virtual system for each agent.For the unknown control gain of the controller,the Nussbaum function is employed.Then,a fuzzy adaptive observer is designed to estimate the unmeasured state by leveraging the general approximation capabilities of fuzzy logic systems.Using the Lyapunov stability method and backstepping technique,we develop the adaptive law and a new distributed controller.This ensures that the outputs of multi-agent systems converge to optimal values.Finally,a simulation example is used to confirm the viability of the presented control mechanism. 展开更多
关键词 distributed optimization nonlinear multi-agent systems(MASs) fuzzy state observer predefined accuracy control
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Multi-objective trajectory optimization for spaceborne antennas with nonlinear coupling using hp-adaptive pseudospectral discretization 认领 引用
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作者 Feng GAO Guanghui SUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期517-530,共14页
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres... Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning. 展开更多
关键词 hp-adaptive pseudospectral method Multi-objective trajectory optimization Nonlinear dynamics Rigid-flexible coupling Spaceborne antenna Structural vibration suppression
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PROMPTx-PE:Adaptive Optimization of Prompt Engineering Strategies for Accuracy and Robustness in Large Language Models 认领 引用
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作者 Talha Farooq Khan Fahad Ali +2 位作者 Majid Hussain Lal Khan Hsien-Tsung Chang 《Computers, Materials & Continua》 SCIE EI 2026年第5期685-715,共31页
The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streaml... The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs. 展开更多
关键词 Prompt engineering large language models adaptive optimization robustness multi-objective optimization reinforcement learning natural language processing
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Adaptive Meta-Loss Networks:Learning Task-Agnostic Loss Functions via Evolutionary Optimization 认领 引用
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作者 Mirna Yunita Xiabi Liu +1 位作者 Zhaoyang Hai Rachmat Muwardi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1931-1949,共19页
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc... Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability. 展开更多
关键词 Meta-learning adaptive loss function task-agnostic optimization evolutionary strategy genetic algorithm classification
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Enhanced Prostate Tumor Segmentation in MRI Using Hybrid Optimization and Adaptive Thresholding 认领 引用
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作者 Wafa Gtifa Ayoub Mhaouch Anis Sakly 《iRADIOLOGY》 CSCD 2026年第2期157-170,共14页
Background:Accurate segmentation of prostate tumors in magnetic resonance imaging(MRI)is critical for improving diagnostic accuracy and supporting clinical decision making.However,many existing approaches rely on supe... Background:Accurate segmentation of prostate tumors in magnetic resonance imaging(MRI)is critical for improving diagnostic accuracy and supporting clinical decision making.However,many existing approaches rely on supervised learning methods that require large annotated datasets and substantial computational resources,limiting their clinical applicability.This study aims to develop and evaluate a fully unsupervised framework for prostate tumor segmentation in multiparametric MRI using hybrid optimization and adaptive thresholding techniques.Methods:This study proposes an unsupervised prostate tumor segmentation framework based on hybrid optimization and adaptive thresholding.Two metaheuristic optimization algorithms,chaotic particle swarm optimization and forest optimization,were employed to optimize Otsu's variance-based thresholding and Kapur's entropy-based thresholding,resulting in four hybrid configurations.The framework was evaluated using multiparametric prostate MRI datasets,including apparent diffusion coefficient,T2-weighted,and diffusion-weighted imaging sequences.Segmentation performance was assessed using overlapbased and classification-based metrics.Statistical analysis included the computation of descriptive performance measures and confidence intervals to evaluate robustness and consistency across datasets.Results:The proposed framework demonstrated reliable and consistent segmentation performance across all MRI modalities.The Otsu-based hybrid configurations showed superior overlap and classification performance in diffusion-based imaging,whereas the entropy-based methods exhibited more conservative behavior on heterogeneous T2-weighted images.Overall,the optimization-based approaches achieved high segmentation accuracy and stability without the need for annotated training data.Conclusions:The proposed hybrid optimization and thresholding framework provides an effective,fully unsupervised solution for prostate tumor segmentation in multiparametric MRI.Its robustness,computational efficiency,and independence from training data highlight its potential for integration into clinical prostate cancer diagnostic workflows. 展开更多
关键词 adaptive thresholding magnetic resonance imaging optimization algorithms prostate cancer tumor segmentation
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DRAGON-MINE:Deep Reinforcement Adaptive Gradient Optimization Network for Mining Rare Events in Healthcare 认领 引用
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作者 Mohammed Abdullah Alsuwaiket 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期967-996,共30页
The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequence... The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequences.This paper presents a new framework,Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events(DRAGON-MINE),to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events.The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an adaptive gradient optimizer to follow novel learning rates.With extensive experiments on the MIMIC-IV and eICU-CRD datasets,DRAGON-MINE consistently outperforms recent state-of-the-art methods for sepsis,cardiac arrest,and adverse drug reaction prediction,achieving AUROC values of 92.3%and 91.6%for sepsis prediction on MIMIC-IV and eICU-CRD,respectively,while consistently outperforming Transformer-,CNN-RNN-,and Fed-Ensemble-based methods across all evaluated tasks and datasets,with particularly strong gains observed in precision-recall performance under severe class imbalance.With its high sensitivity(88.4%)and specificity(90.2%),DRAGON-MINE enables reliable early warning of rare clinical events in critical care settings while minimizing false alarms,supporting safer clinical decision support systems,and demonstrating strong potential for scalable deployment across multi-institutional intensive care environments through federated learning. 展开更多
关键词 Deep reinforcement learning rare event prediction class imbalance healthcare AI adaptive gradient optimization sepsis detection federated learning
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An Efficient Federated Learning Optimization Approach Based on Adaptive Hybrid Model Pruning 认领 引用
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作者 MengDie Hu Na Wang +2 位作者 XueHui Du BaiDong Huang KaiYuan Wang 《Computers, Materials & Continua》 SCIE EI 2026年第9期857-881,共25页
With the rapid development of the Internet of Things(IoT)and edge intelligence,the volume of data generated by edge devices has grown explosively.Federated learning(FL),characterized by the paradigm of“data remaining... With the rapid development of the Internet of Things(IoT)and edge intelligence,the volume of data generated by edge devices has grown explosively.Federated learning(FL),characterized by the paradigm of“data remaining local while models are shared,”has emerged as a key approach for adapting to the distributed architecture of edge computing,breaking down data silos,and enabling privacy preservation.However,its practical deployment in edge computing environments still faces significant challenges,including limited device resources and pronounced data heterogeneity.Existing pruning strategies for federated learning are predominantly based on static and single-design schemes,making it difficult to achieve a balanced trade-off among training overhead,communication cost,and model accuracy.To address these issues,this paper proposes FedAHP(Federated Learning with Adaptive Hybrid Pruning),an efficiency optimization scheme for federated learning based on adaptive hybrid model pruning.On the client side,an adaptive pruning mechanism driven by training states is designed to dynamically adjust pruning behavior during local training.On the server side,a counter-based heterogeneous aggregation method is adopted to efficiently align updates from clients with different pruning rates,thereby avoiding additional communication overhead.Furthermore,after training becomes stable,a performance-aware periodic structured pruning strategy is introduced to compress the global model scale and reduce subsequent training costs.Experimental results demonstrate that FedAHP maintains high model accuracy on the MNIST,CIFAR-10 and CIFAR-100 datasets while significantly reducing per-round communication overhead and time cost,making it well suited to the resource-constrained requirements of edge computing scenarios. 展开更多
关键词 Federated learning edge computing model pruning efficiency optimization
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A Novel Adaptive Deep Learning-Based Intrusion Detection System Using Particle Swarm Optimization 认领 引用
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作者 Soukaina Mjahed Ouail Mjahed 《Computers, Materials & Continua》 SCIE EI 2026年第7期2129-2146,共18页
The rapid emergence of sophisticated,dynamic,and rare or previously unseen attack pattern exposes fundamental limitations of conventional intrusion detection systems(IDS)based on static learning architectures.While de... The rapid emergence of sophisticated,dynamic,and rare or previously unseen attack pattern exposes fundamental limitations of conventional intrusion detection systems(IDS)based on static learning architectures.While deep learning(DL)models have demonstrated strong performance by capturing complex spatial and temporal traffic patterns,existing DL-based IDS largely rely on fixed decision structures,restricting adaptability to evolving threats.Furthermore,current hybrid DL-metaheuristic approaches typically use such metaheuristics as offline or auxiliary optimizers,without interacting with the deep model’s internal latent representations.This paper introduces a novel co-evolutionary IDS that establishes a tight,bidirectional coupling between DL and Particle Swarm Optimization(PSO)through latent-space-guided structural adaptation.A CNN-LSTM(Convolutional Neural Networks-Long Short-Term Memory)encoder learns discriminative spatial-temporal representations of network traffic,which dynamically guide PSO to select and optimize Adaptive Decision Blocks during training.Unlike prior hybrid methods,the proposed framework enables continuous co-evolution of both representation learning and decision structure,allowing the IDS to adapt its internal architecture in response to uncertain,rare,and previously unseen attack patterns.Comprehensive evaluations on UNSW-NB15,CICIDS2017,and ToN-IoT demonstrate statistically significant improvements over state-of-the-art DL and hybrid IDS approaches,achieving over 99.97%accuracy,recall and F1-score,and low-latency inference suitable for near real-time deployment. 展开更多
关键词 Classification CNN PSO intrusion detection system adaptive deep learning
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Adaptive Optimization of Drainage Processes in High-Water-Cut Tight Gas Reservoirs 认领 引用
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作者 Jiaming Cai Xiongxiong Wang +2 位作者 Xianwen Wang Zhengyan Zhao Youliang Jia 《Fluid Dynamics & Materials Processing》 EI 2026年第3期131-149,共19页
To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight san... To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight sandstone gas reservoirs,this study systematically investigates the graded optimization and dynamic adaptation of drainage gas recovery technologies.Production data from a representative tight gas field were first employed to forecast reservoir performance.The predictive reliability was rigorously validated through high-precision history matching,thereby providing a quantitatively consistent foundation for subsequent wellbore optimization.Building on this characterization,a coupled simulation framework was developed that integrates wellbore multiphase flow modeling with nodal analysis based on the Inflow Performance Relationship,IPR,and the Vertical Lift Performance,VLP.This coordinated approach enables comprehensive evaluation of process adaptability and dynamic optimization of foam-assisted drainage,mechanical pumping,and jet pumping systems under evolving water-gas ratio,WGR conditions.The results reveal that a progressively increasing water-gas ratio is the dominant factor driving the transition from chemically assisted drainage methods to mechanically enhanced lifting technologies.A distinct quantitative threshold is identified at WGR≈0.002,beyond which mechanical intervention becomes more effective and economically justified.For mechanical pumping and jet pumping systems,a parameter inversion optimization strategy constrained by the target bottomhole flowing pressure,Pwf,is proposed to ensure stable production while maintaining reservoir drawdown control.In particular,the nozzle-to-throat area ratio of the jet pump is identified as the key governing parameter influencing entrainment capacity and lifting efficiency.Moreover,a configuration characterized by small pump diameter,long stroke length,and low operating speed is demonstrated to satisfy drainage requirements while mitigating torque fluctuations,enhancing volumetric efficiency,and improving pump fillage stability. 展开更多
关键词 High water-cut tight sandstone gas reservoir IPR/VLP node analysis drainage gas production process optimization
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An Adaptive Cubic Regularisation Algorithm Based on Affine Scaling Methods for Constrained Optimization 认领 引用
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作者 PEI Yonggang WANG Jingyi 《应用数学》 北大核心 2026年第1期258-277,共20页
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op... In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported. 展开更多
关键词 Constrained optimization Adaptive cubic regularisation Affine scaling Global convergence
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Efficient Adaptive Collision-Free Path Planning Algorithm for Indoor Mobile Robots:Optimization and Application 认领 引用
19
作者 Xiangyang Lu Yandan Wang +1 位作者 Guangyi Zang Junqi Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期205-217,共13页
An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth... An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth paths,and poor dynamic obstacle avoidance capability.Through an“8+5”neighborhood screening,a 16-neighborhood evaluation function,and a second-order then third-order Bézier curve optimization process,a Jetson Nano+ROS(Robot Operating System)is deployed to meet the requirements of efficient and safe navigation for fire inspection robots in complex environments.The results show that,compared with the original algorithm,the proposed algorithm reduces the average number of traversed nodes by 49.23%,the number of turns in the optimized path has decreased by approximately 28.82%,decreases curvature by 66.6%,and eliminates path tangency with obstacles.This also supports real-time obstacle avoidance with integration DWA,and outperforms traditional methods. 展开更多
关键词 adaptive path planning improved A*algorithm Bézier curve optimization fire inspection robot Jetson Nano+ROS system
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
20
作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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