期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到6,497篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
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
暂未订购 下载PDF
Aerodynamic/control coupling optimization method for reusable vehicles using adaptive partitioning 认领 引用
2
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
暂未订购 下载PDF
Disturbance observer-based constrained adaptive fuzzy control for manipulator:a cooperative optimization approach of gain 认领 引用
3
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
4
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
暂未订购 下载PDF
PROMPTx-PE:Adaptive Optimization of Prompt Engineering Strategies for Accuracy and Robustness in Large Language Models 认领 引用
5
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
暂未订购 下载PDF
Adaptive Meta-Loss Networks:Learning Task-Agnostic Loss Functions via Evolutionary Optimization 认领 引用
6
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
暂未订购 下载PDF
Enhanced Prostate Tumor Segmentation in MRI Using Hybrid Optimization and Adaptive Thresholding 认领 引用
7
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
暂未订购 下载PDF
DRAGON-MINE:Deep Reinforcement Adaptive Gradient Optimization Network for Mining Rare Events in Healthcare 认领 引用
8
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
暂未订购 下载PDF
An Adaptive Cubic Regularisation Algorithm Based on Affine Scaling Methods for Constrained Optimization 认领 引用
9
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
暂未订购 下载PDF
Efficient Adaptive Collision-Free Path Planning Algorithm for Indoor Mobile Robots:Optimization and Application 认领 引用
10
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
暂未订购 下载PDF
Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
11
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)
暂未订购 下载PDF
H∞Optimal Output Regulation of Unknown Linear Systems via an Adaptive Dynamic Programming and Internal Model 认领 引用
12
作者 Yong-Sheng Ma Jian Sun Yong Xu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1314-1324,共11页
This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal con... This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal control policy and a worst-case disturbance policy through the formulation and solution of a zero-sum game problem.Subsequently,leveraging adaptive dynamic programming,we propose a policy iteration learning algorithm capable of learning both the optimal control policy and the worst-case disturbance policy directly from system data.The existing algorithms necessitate an initial stabilizing policy,a full-rank condition,and the storage of historical data to guarantee algorithm convergence.In contrast,we design a dual policy iteration algorithm equipped with an online learning mechanism,thereby eliminating these additional prerequisites.Simulation results with an antonomous ground vehicle underscore the effectiveness of our proposed algorithm,and its superiority is further demonstrated through comparisons with existing methodologies. 展开更多
关键词 Adaptive dynamic programming internal model principle optimal control output regulation
暂未订购 下载PDF
ADAPT:A Model-Free Adaptive Optimal Control for Continuum Robots 认领 引用
13
作者 Haiyang Fang Sishen Yuan +2 位作者 Hongliang Ren Shuping He Shing Shin Cheng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期205-217,共13页
Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft conti... Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm. 展开更多
关键词 Adaptive optimal control continuum robots(CRs) Hamilton-Jacobi-Bellman(HJB)equation model-free approach
暂未订购 下载PDF
Bi-Directional Evolutionary Topology Optimization with Adaptive Evolutionary Ratio for Nonlinear Structures 认领 引用 被引量：1
14
作者 Linli Tian Wenhua Zhang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第5期337-350,共14页
Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper propos... Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper proposes an improved bi-directional evolutionary structural optimization(BESO)method tailored for maximizing stiffness in nonlinear structures.The optimization program is developed in Python and can be combined with Abaqus software to facilitate finite element analysis(FEA).To accelerate the speed of optimization,a novel adaptive evolutionary ratio(ER)strategy based on the BESO method is introduced,with four distinct adaptive ER functions proposed.The Newton-Raphson method is utilized for iteratively solving nonlinear equilibrium equations,and the sensitivity information for updating design variables is derived using the adjoint method.Additionally,this study extends topology optimization to account for both material nonlinearity and geometric nonlinearity,analyzing the effects of various nonlinearities.A series of comparative studies are conducted using benchmark cases to validate the effectiveness of the proposed method.The results show that the BESO method with adaptive ER significantly improves the optimization efficiency.Compared to the BESO method with a fixed ER,the convergence speed of the four adaptive ER BESO methods is increased by 37.3%,26.7%,12%and 18.7%,respectively.Given that Abaqus is a powerful FEA platform,this method has the potential to be extended to large-scale engineering structures and to address more complex optimization problems.This research proposes an improved BESO method with novel adaptive ER,which significantly accelerates the optimization process and enables its application to topology optimization of nonlinear structures. 展开更多
关键词 Topology optimization Adaptive evolutionary ratio BESO method Nonlinear
暂未订购 下载PDF
Dynamic Multi-Objective Gannet Optimization(DMGO):An Adaptive Algorithm for Efficient Data Replication in Cloud Systems 认领 引用
15
作者 P.William Ved Prakash Mishra +3 位作者 Osamah Ibrahim Khalaf Arvind Mukundan Yogeesh N Riya Karmakar 《Computers, Materials & Continua》 SCIE EI 2025年第9期5133-5156,共24页
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat... Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance. 展开更多
关键词 Cloud computing data replication dynamic optimization multi-objective optimization gannet optimization algorithm adaptive algorithms resource efficiency scalability latency reduction energy-efficient computing
暂未订购 下载PDF
Adaptive Multi-strategy Rabbit Optimizer for Large-scale Optimization 认领 引用
16
作者 Baowei Xiang Yixin Xiang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第1期398-416,共19页
As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for th... As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for these algorithms.In this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization challenges.It employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy search.Key parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational efficiency.We offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and independent.RA is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000.The results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and scalability.Finally,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020. 展开更多
关键词 Adaptive parameter Large scale optimization Rabbit algorithm Swarm intelligence Engineering optimization
Second-Life Battery Energy Storage System Capacity Planning and Power Dispatch via Model-Free Adaptive Control-Embedded Heuristic Optimization 认领 引用
17
作者 Chuan Yuan Chang Liu +5 位作者 Shijun Chen Weiting Xu Jing Gou Ke Xu Zhengbo Li Youbo Liu 《Energy Engineering》 EI 2025年第9期3573-3593,共21页
The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg... The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches. 展开更多
关键词 Second-life battery energy storage systems model-free adaptive voltage control bilevel optimization framework heterogeneous battery degradation model heuristic capacity configuration optimization
暂未订购 下载PDF
A body-fitted adaptive mesh and Helmholtz-type filter based parameterized level-set method for structural topology optimization 认领 引用 被引量：2
18
作者 Yijie Lu Xueying Chang +3 位作者 Zhengwei Zhang Hui Liu Yanguo Zhou Hao Li 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第5期131-147,共17页
Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation o... Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation of new holes.However,most pertinent investigations in the field rely predominantly on fixed background mesh,which is never remeshed.Consequently,the mesh element partitioned by material interface during the optimization process necessitates approximation by using artificial interpolation models to obtain its element stiffness or other properties.This paper introduces a novel approach to topology op-timization by integrating the PLSM with body-fitted adaptive mesh and Helmholtz-type filter.Primarily,combining the PLSM with body-fitted adaptive mesh enables the regeneration of mesh based on the zero level-set interface.This not only precludes the direct traversal of the material interface through the mesh element during the topology optimization process,but also improves the accuracy of calculation.Additionally,the incorporation of a Helmholtz-type partial differential equation filter,relying solely on mesh information essential for finite element discretization,serves to regulate the topological complexity and the minimum feature size of the optimized structure.Leveraging these advantages,the topology optimization program demonstrates its versa-tility by successfully addressing various design problems,encompassing the minimum mean compliance problem and minimum energy dissipation problem.Ultimately,the result of numerical example indicates that the optimized structure exhibits a dis-tinct and smooth boundary,affirming the effective control over both topological complexity and the minimum feature size of the optimized structure. 展开更多
关键词 Topology optimization Parameterized level-set method Helmholtz-type filter Body-fitted adaptive mesh
暂未订购 下载PDF
Wind Power Prediction Model based on Integrated Osprey and Adaptive T-distribution Dung Beetle Optimization Algorithm 认领 引用 被引量：1
19
作者 Yanyan Wu Ying Xu Xudong Huang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第5期2678-2699,共22页
Accurate forecasting of wind power is crucial for ensuring the reliable operation of the electrical grid.Due to the impact of various factors,wind power forecasting presents a significant challenge.This paper presents... Accurate forecasting of wind power is crucial for ensuring the reliable operation of the electrical grid.Due to the impact of various factors,wind power forecasting presents a significant challenge.This paper presents the model that integrates Osprey and adaptive T-distribution dung beetle algorithm for optimizing a convolutional neural network.The CNN-BiLSTM-Attention model combines bidirectional long short-term memory neural networks with an attention mechanism,thereby improving the accuracy of wind power generation predictions.The original data is subjected to Variational Mode Decomposition(VMD)for analysis,taking into account the fluctuations in wind power across different periods.The BiLSTM network with short-term memory processes time-series wind power data,yielding an optimal predictive performance.The integration of the osprey algorithm and adaptive T-distribution within the Dung Beetle Optimization Algorithm was utilized to optimize the hyperparameters of the CNN-BiLSTM-Attention model,thereby enhancing its predictive performance.To assess the efficacy of the CNN-BiLSTM-Attention algorithm,enhanced by Ospreys and adaptive T-distributed dung beetle algorithm,we conducted experiments using the CEC2021 benchmark function.The integrated Osprey and adaptive T-distribution Dung Beetle algorithm has excellent global optimization performance when dealing with complex optimization problems.The fusion of Osprey and the adaptive T-distribution Dung beetle algorithm optimized the CNN-BiLSTM-Attention algorithm as well as other optimization algorithms for ablation experiments.The results show that the improved algorithm performs well in predicting wind power.The experimental findings suggest that the model’s predictive efficiency has enhanced by a minimum of 17.74%. 展开更多
关键词 Convolutional neural network Bidirectional long term memory Dung beetle optimization IntegratedOsprey and adaptive T-distribution
Risk-Aware Adaptive Federated Learning for Cyber-Secure Edge-AI in Smart Edge-IoT Environments 认领 引用
20
作者 Tanveer Ahmad Tahani Alsubait +2 位作者 Amina Salhi Amani Ibraheem Muhammad Asim Saleem 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1439-1461,共23页
The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning,data heterogeneity,and adversarial manipulation.This pap... The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning,data heterogeneity,and adversarial manipulation.This paper proposes a new risk-aware adaptive learningmodel that federated Edge-AI systems explicitly simulates cyber risk in the process of local training and global aggregation.The proposed solution combines stochastic optimization and adversarial risk bounding with adaptive gradient correction to develop strong learning in non-IID data distributions and malicious client behavior.Convergence guarantees are defined by the theoretical analysis in the case of limited adversarial perturbations.The proposed framework achieves up to 95%detection accuracy and demonstrates more than 20%improvement in robustness,where robustness is defined as the relative degradation in detection performance under adversarial perturbations.The performance is evaluated against state-of-the-art baselines,including HADA-FL and centralized training on the Edge-IIoTset dataset,with results reported as averages over multiple randomized runs.Furthermore,the model converges within 50 communication rounds,which corresponds to a fixed training horizon rather than an early-stopping criterion.These findings demonstrate the usefulness of risk-sensitive adaptive learning in safe and trustworthy Edge-AI implementation in a new generation edge-IoT environment. 展开更多
关键词 Edge AI federated learning(FL) adaptive learning adversarial robustness distributed optimization
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈