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Gradient boosting:A computationally efficient alternative to Markov chain Monte Carlo sampling for fitting large Bayesian spatio-temporal binomial regression models 认领 引用 被引量：1
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作者 Rongjie Huang Christopher McMahan +3 位作者 Brian Herrin Alexander McLain Bo Cai Stella Self 《Infectious Disease Modelling》 CSCD 2025年第1期189-200,共12页
Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte ... Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo(MCMC)methods are commonly used for such data.When the spatio-temporal support of the model is large,implementing an MCMC algorithm becomes a significant computational burden.This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatiotemporal mixed effects binomial regression model.We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach.Both methods are used to produce monthly forecasts for Lyme disease,anaplasmosis,ehrlichiosis,and heartworm disease in domestic dogs for the contiguous United States.The data have a spatial support of 3108 counties and a temporal support of 108e138 months with 71e135 million test results.The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm,with a similar mean absolute prediction error. 展开更多
关键词 historical testing data gradient boosting fitting models bayesian spatiotemporal markov chain monte carlo mcmc methods gradient boosting algorithm disease forecasting surveillance computationally efficient
Classification- and Regression-Assisted Differential Evolution for Computationally Expensive Problems 认领 引用 被引量：8
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作者 陆晓芬 唐珂 《Journal of Computer Science & Technology》 SCIE EI CSCD 2012年第5期1024-1034,共11页
Differential Evolution （DE） has been well accepted ever, it usually involves a large number of fitness evaluations to as an effective evolutionary optimization technique. Howobtain a satisfactory solution. This disa... Differential Evolution （DE） has been well accepted ever, it usually involves a large number of fitness evaluations to as an effective evolutionary optimization technique. Howobtain a satisfactory solution. This disadvantage severely restricts its application to computationally expensive problems, for which a single fitness evaluation can be highly timeconsuming. In the past decade, a lot of investigations have been conducted to incorporate a surrogate model into an evolutionary algorithm （EA） to alleviate its computational burden in this scenario. However, only limited work was devoted to DE. More importantly, although various types of surrogate models, such as regression, ranking, and classification models, have been investigated separately, none of them consistently outperforms others. In this paper, we propose to construct a surrogate model by combining both regression and classification techniques. It is shown that due to the specific selection strategy of DE, a synergy can be established between these two types of models, and leads to a surrogate model that is more appropriate for DE. A novel surrogate model-assisted DE, named Classification- and Regression-Assisted DE （CRADE） is proposed on this basis. Experimental studies are carried out on a set of 16 benchmark functions, and CRADE has shown significant superiority over DE-assisted with only regression or classification models. Further comparison to three state-of-the-art DE variants, i.e., DE with global and local neighborhoods （DECL）, JADE, and composite DE （CODE）, also demonstrates the superiority of CRADE. 展开更多
关键词 surrogate model differential evolution computationally expensive problem
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The three-dimensional meshfree numerical manifold method based on parallel computing 认领 引用
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作者 Keqin Zhang Wei Wu +2 位作者 Danfeng Zhang Yanfei Kang Hehua Zhu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期360-371,共12页
The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challe... The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challenge,the meshfree numerical manifold method is developed by integrating the moving least-squares method into the numerical manifold method,effectively bypassing the need for meshing complex geometric objects.However,the implementation of the moving least-squares method introduces computational efficiency issues.To mitigate these,parallel computing methods have been incorporated,resulting in a tenfold increase in the speed of assembling the stiffness matrix with central processing unit parallelism,and a twentyfold increase with graphics processing unit parallelism.The static mechanical system equations for the meshfree numerical manifold method are derived using the Galerkin method.The method’s effectiveness and accuracy are then validated through a series of numerical experiments.The experiments demonstrated that the meshfree numerical manifold method achieves a high precision with minimal nodes and integration points.Additionally,positioning nodes outside the domain significantly improves computational accuracy at the boundaries. 展开更多
关键词 Meshfree numerical manifold method Three-dimensional computation Parallel computation Elastostatics Moving least-squares
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Secformer:Privacy-preserving atomic-level componentized transformer-like model with MPC 认领 引用
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作者 Chi Zhang Tao Shen +3 位作者 Fenhua Bai Kai Zeng Xiaohui Zhang Bin Cao 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期86-100,共15页
The global surge in Artificial Intelligence(AI)has been triggered by the impressive performance of deep-learning models based on the Transformer architecture.However,the efficacy of such models is increasingly depende... The global surge in Artificial Intelligence(AI)has been triggered by the impressive performance of deep-learning models based on the Transformer architecture.However,the efficacy of such models is increasingly dependent on the volume and quality of data.Data are often distributed across institutions and companies,making cross-organizational data transfer vulnerable to privacy breaches and subject to privacy laws and trade secret regulations.These privacy and security concerns continue to pose major challenges to collaborative training and inference in multi-source data environments.These challenges are particularly significant for Transformer models,where the complex internal encryption computations drastically reduce computational efficiency,ultimately threatening the model's practical applicability.We hence introduce Secformer,an innovative architecture specifically designed to protect the privacy of Transformer-like models.Secformer separates the encoder and decoder modules,enabling the decomposition of computation flows in Transformer-like models and their efficient mapping to Multi-Party Computation(MPC)protocols.This design effectively addresses privacy leakage issues during the collaborative computation process of Transformer models.To prevent performance degradation caused by encrypted attention modules,we propose a modular design strategy that optimizes high-level components by reconstructing low-level operators.We further analyze the security of Secformer's core components,presenting security definitions and formal proofs.We construct a library of fundamental operators and core modules using atomic-level component designs as the basic building blocks for encoders and decoders.Moreover,these components can serve as foundational operators for other Transformer-like models.Extensive experimental evaluations demonstrate Secformer's excellent performance while preserving privacy and offering universal adaptability for Transformer-like models. 展开更多
关键词 Privacy-preserving computation Deep learning Multi-party computation Data sharing
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Inorganic all-solid-state sodium batteries:Electrolyte design,interface engineering,and multiscale approaches 认领 引用
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作者 Yihang Song Hanyu Zhou +12 位作者 Tingyi Zhao Boyang Zhang Huanting Sun Iqbal Ahmed Khurshid Jiajia Wang Hao Li Yanqiang Kong Lei Chen Liu Cui Dongyue Zhang Weijia Wang Lijun Yang Xiaoze Du 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期415-434,I0010,共20页
In the realm of large-scale power system energy storage,sodium-based batteries represent a cost-effective post-lithium energy storage technology,making inorganic solid-state sodium batteries(ISSSB)a critical branch of... In the realm of large-scale power system energy storage,sodium-based batteries represent a cost-effective post-lithium energy storage technology,making inorganic solid-state sodium batteries(ISSSB)a critical branch of this development.Inorganic solid-state electrolytes(ISSEs)are the core components of sodium batteries;however,they face significant challenges such as insufficient ionic conductivity,interfacial instability,and dendrite growth,all of which severely hinder practical application.This review critically assesses experimental protocols and theoretical frameworks related to mainstream ISSEs and systematizes optimization strategies aimed at overcoming these challenges.Leveraging integrated insights from both experimental and computational studies,the review first categorizes and summarizes the primary types of ISSEs,namely oxide-,sulfide-,and halide-based electrolytes.It then details interfacial optimization strategies focused on addressing three core interfacial issues:ion transport barriers resulting from mechanical incompatibility,side reactions stemming from electrochemical mismatch,and dendrite formation.Finally,the review advocates prioritizing in-depth research that integrates experimental and theoretical approaches to establish a closed-loop methodology encompassing predictive design,multiscale investigation,mechanistic exploration,and high-throughput automated experimentation,with feedback-driven refinement.This work serves as a comprehensive reference and systematic roadmap for future research on solid-state electrolytes(SSEs). 展开更多
关键词 Sodium battery Inorganic solid-state electrolytes Modification strategy Experimental modification Theoretical computation Interface engineering
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Machine Learning-Based Prediction of Rock Fracture under Uniaxial Loading Using Infrared Radiation 认领 引用
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作者 Naseer Muhammad Khan Liqiang Ma +4 位作者 Majid Khan Sajjad Hussain Waleed Inqiad Tariq Feroze Danial Jahed Armaghani 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期465-505,共41页
Rock fracture behavior under stress is vital for risk evaluation in underground engineering excavation because the presence of water can significantly increase the extent of cracks and fractures in rock,leading to str... Rock fracture behavior under stress is vital for risk evaluation in underground engineering excavation because the presence of water can significantly increase the extent of cracks and fractures in rock,leading to structural damage.This can result in catastrophic failures,including rock bursts,coal bursts,and water inrush.Hence,reliable prediction of rock damage and fracture processes is still lacking,which,in turn,enables the safe and efficient conduct of engineering projects in rock-mass environments.Thus,this study examines both dry and saturated sandstone samples under loading using Infrared Radiation(IR),Acoustic Emission(AE)monitoring,and Particle Flow Computation(PFC)techniques to effectively evaluate the fracture process in rocks under loading.Additionally,seven different artificial intelligence techniques,such as Gene Expression Programming(GEP),Gradient Boost Regression(GBR),Extreme Gradient Boosting(XGB),Adaptive Boosting(AdaBoost),Light Gradient Boosting Machine(LGBM),Categorical Boosting(CatBoost),were employed along with Explainable Machine Learning(XML)to predict the rock damage and fracture process.These models helped in the development of early warning signals to prevent catastrophic accidents.Both the experimental and simulation results have shown that the fracture density measured in terms of PFC and AE cumulative energy is significant in the saturated conditions compared to the dry conditions.Also,stress levels of 0.72 and 0.75 were found to be the warning signs in both dry and saturated conditions,based on the IR index(Average Infrared Radiation Temperature,AIRT)and AE characteristics.The comparison showed that the prediction accuracy of the XGB algorithm was the highest,followed by GBR,CatBoost,LGBM,GEP,and AdaBoost.However,GEP expressed its output in the form of an empirical equation owing to its grey-box nature,and thus,the law of fracture estimation in the form of an empirical equation was developed.The XML methods were added in order to enhance the interpretability of the high-performing,but black-box,XGB model.Such methods,along with a user-friendly Graphical User Interface(GUI),improved the model transparency and facilitated the integration of data-driven decision-making.XML and GUI tools may be instrumental in improving the safety measures adopted in coal mines and tunnels by reducing the risks and increasing operational safety. 展开更多
关键词 Rock fracture prediction acoustic emission infrared radiation particle flow computation water contents
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A Computational Modeling Framework for Verifiable Computation Offloading in Resource-Constrained IoT Smart Contract Systems Using Zero-Knowledge and Fuzzy Logic 认领 引用
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作者 Hong Min Yousef Ibrahim Daradkeh +2 位作者 Jung Taek Seo Mohd Anjum Sana Shahab 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1488-1520,共33页
This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things(IoT)-enabled smart contract systems.The integration of IoT,edge computing,and blockchain int... This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things(IoT)-enabled smart contract systems.The integration of IoT,edge computing,and blockchain introduces significant challenges,including limited device capacity,high verification cost,and scalability constraints.Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices,resulting in increased latency,energy consumption,and transaction costs.To address these issues,this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup(Z-FLOR)framework,an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems.The proposed framework integrates three key components.First,a zero-knowledge proof-based verification model using the Grothl6 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification.Second,a Fuzzy Logic-Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices,edge servers,and cloud platforms based on energy availability,network delay,and device reliability.Third,an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability.Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework.Results indicate that Z-FLOR achieves 99.7%verification accuracy and 98.9%proof compression efficiency,while gas cost analysis indicates gas cost reductions in the range of 80%-98%.Z-FLOR additionally achieves a 44.0%reduction in latency,5l.0%savings in gas costs,and 38.0%energy consumption compared to baseline approaches.These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments. 展开更多
关键词 Zero-knowledge proofs zkSNARK IoT smart contracts verifiable computation blockchain scalability edge computing rollup architecture
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Enhancing the performance of quantum battery by squeezing reservoir engineering 认领 引用
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作者 Yue Li Rong-Fang Liu +2 位作者 Jia-Bin You Wan-Li Yang Hua Guan 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第1期226-233,共8页
Reservoir engineering has been widely used in various quantum technologies.Based on a cavity-QED(quantum electrodynamics)model,we propose a potentially practical scheme using squeezed-vacuum reservoir engineering to o... Reservoir engineering has been widely used in various quantum technologies.Based on a cavity-QED(quantum electrodynamics)model,we propose a potentially practical scheme using squeezed-vacuum reservoir engineering to optimize the performance of a quantum battery(QB)located inside a cavity driven by a broadband squeezed laser,which acts as a squeezed-vacuum reservoir.Using the reduced master equation of the QB obtained via the adiabatic elimination method,we focus on the QB's charging dynamics under tunable squeezed reservoirs governed by parametrically controlled squeezing parameters,which dictate the efficiency of energy transfer and the extractable work(ergotropy)of the QB.We show that increasing the squeezing strength improves the charging rate and enables rapid energy transfer,whereas the steady-state energy of the QB saturates at specific values of the squeezing parameter.Notably,the ergotropy of the QB reaches its maximum at a critical squeezing strength and does not scale monotonically with the squeezing strength.This nonmonotonic behavior underscores the existence of optimal parameter regimes,through which the performance of the QB can be significantly enhanced. 展开更多
关键词 quantum computation cavity quantum electrodynamics
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A Multi-Objective Deep Reinforcement Learning Algorithm for Computation Offloading in Internet of Vehicles 认领 引用
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作者 Junjun Ren Guoqiang Chen +1 位作者 Zheng-Yi Chai Dong Yuan 《Computers, Materials & Continua》 SCIE EI 2026年第1期2111-2136,共26页
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain... Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively. 展开更多
关键词 Deep reinforcement learning internet of vehicles multi-objective optimization cloud-edge computing computation offloading service caching
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Hyperbolic cosine transformer for LiDAR 3D object detection 认领 引用
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作者 Jigang TONG Fanhang YANG +1 位作者 Sen YANG Shengzhi DU 《Optoelectronics Letters》 EI 2026年第3期160-166,共7页
Previous point-wise methods are suffering from time consumption and limited receptive fields to capture information among points.To address these limitations,we propose the cosh-attention,which reduces the computation... Previous point-wise methods are suffering from time consumption and limited receptive fields to capture information among points.To address these limitations,we propose the cosh-attention,which reduces the computation complexity of space and time from the quadratic order to linear order with respect to the number of points.In the cosh-attention,the traditional softmax operator is replaced by non-negative Re LU activation and hyperbolic-cosine-based operator with re-weighting mechanism.Then based on the key component,cosh-attention,we present a two-stage hyperbolic cosine transformer(ChTR3D)for 3D object detection from point clouds.It refines proposals by applying cosh-attention in linear computation complexity to encode rich contextual relationships among points.Extensive experiments on the widely used KITTI dataset and Waymo Open Dataset demonstrate that compared with vanilla attention,the cosh-attention significantly improves the inference speed with competitive performance.Among two-stage state-of-the-art methods using point-level features for refinement,the proposed ChTR3D is the fastest one. 展开更多
关键词 key co lidar d object detection point wise methods computation complexity hyperbolic cosine transformer cosh attention space time softmax operator
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On a(2+1)-Dimensional Generalized Variable-Coefficient Date-Jimbo-Kashiwara-Miwa Equation with the Marine-Engineering and Ocean-Dynamics Applications 认领 引用
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作者 GAO Xin-yi LIU Jian-guo WANG Gang-wei 《China Ocean Engineering》 SCIE EI CSCD 2026年第3期695-703,共9页
Current marine-engineering and ocean-dynamics studies have been very active.On account of marine engineering,ocean dynamics,fluid mechanics,plasma physics and nonlinear optics,we hereby study a(2+1)-dimensional genera... Current marine-engineering and ocean-dynamics studies have been very active.On account of marine engineering,ocean dynamics,fluid mechanics,plasma physics and nonlinear optics,we hereby study a(2+1)-dimensional generalized variable-coefficient Date-Jimbo-Kashiwara-Miwa equation,for which we build up certain auto-Bäcklund transformation via a noncharacteristic movable singular manifold,solitonic solutions,analytic solutions as well as similarity reductions.As for the wave amplitude,our results depend on the variable coefficients,some of which denote the dispersion in space and space-time,separately,while some of which are caused by the geometric or physical inhomogeneities,such as the changing radius and medium density.No variable-coefficient constraints are involved in the analysis.This work may be of some theoretical use in assisting the future studies in marine engineering,ocean dynamics,fluid mechanics,plasma physics and nonlinear optics. 展开更多
关键词 marine engineering ocean dynamics symbolic computation (2+1)-dimensional generalized variablecoefficient Date-Jimbo-Kashiwara-Miwa equation Bäcklund transformation solitons similarity reductions
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Joint computation offloading and service downloading in satellite edge computing networks 认领 引用
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作者 Wu Qi Zhu Lidong 《China Communications》 SCIE EI CSCD 2026年第4期238-258,共21页
The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-ed... The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-edge computing architecture that organizes edge satellites and their associated ground clouds into multiple collaborative domains.Within each domain,we formulate a joint optimization problem for computation offloading and service downloading under the constraints of edge satellites’service deployment and caching space,aiming to minimize the sum of weighted energy consumption and latency.The originally non-convex problem is transformed into a more tractable convex optimization formulation through variable relaxation.Subsequently,we develop an alternating direction method of multipliers(ADMM)-based distributed optimization framework that enables cooperative decision-making among domain satellites for the optimization of computational offloading,service downloading,and service deleting variables.Additionally,we propose an innovative binary variable recovery algorithm that ensures feasible conversion from continuous solutions to discrete decision variables while preserving constraint satisfaction.Extensive simulations demonstrate that our approach achieves lower task execution cost and packet loss rate compared with benchmarks. 展开更多
关键词 alternating direction method of multipliers(ADMMs) cloud-edge computing architecture computation offloading edge satellites service downloading
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Enhancing Efficiency in Lattice-Based Post-Quantum Cryptography with Systolic Array Polynomial Multiplication 认领 引用
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作者 Atef Ibrahim Fayez Gebali 《Computers, Materials & Continua》 SCIE EI 2026年第9期1007-1023,共17页
The ongoing expansion of the Internet of Things(IoT)fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks.Nonetheless,vulnerabilities surrounding sys... The ongoing expansion of the Internet of Things(IoT)fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks.Nonetheless,vulnerabilities surrounding system integrity and data confidentiality present major bottlenecks to widespread adoption,a dilemma severely intensified by impending quantum computing capabilities.Defending these networks demands the integration of post-quantum cryptographic primitives;yet,the severe hardware constraints characterizing peripheral IoT components complicate practical deployment.Quantum-resistant lattice cryptography offers a highly promising pathway to overcome these limitations,largely because the foundational security and throughput of these protocols hinge on polynomial multiplication performance.Consequently,optimizing the computational speed and architectural efficiency of this specific algebraic operation drastically enhances the viability of lattice-reliant defense mechanisms.To address this need,this study develops a specialized systolic array architecture engineered explicitly as an underlying arithmetic engine for polynomial multiplication within the Binary Ring Learning With Errors(BRLWE)protocol.Tailored for low-power hardware security modules(HSMs)situated at the network edge,the proposed circuit achieves rapid modular multiplication while ensuring a highly compact silicon footprint.By aligning the hardware layout with the precise algebraic properties of the BRLWE variation,this approach delivers a scalable,optimized framework for constructing secure IoT networks capable of resisting quantum adversaries,thereby acting as a pivotal building block for resilient industrial edge protection.Additionally,this study aligns with UN Sustainable Development Goals 8 and 9 by fostering digital trust in emerging technological systems and supporting the safe,adaptive growth of modern electronic economies. 展开更多
关键词 Safeguarding IoT networks post-quantum cryptography IoT edge computing systolic computation structures lattice-based security techniques multiplication of polynomials sustainable development goals future economies
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Computationally aware systems:Recommended features through a review of 188 definitions and its relevance to advanced simulation 认领 引用 被引量：1
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作者 Reza Irani Pouria Sharifian Monfared +1 位作者 Mohammad Kazemifard Tuncer Oren 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2021年第1期112-141,共30页
In this paper,188 definitions of aware,awareness,conscious,and consciousness,classified in different categories including cognitive science,computation,medicine,neurology,and philosophy,are reviewed.Based on this syst... In this paper,188 definitions of aware,awareness,conscious,and consciousness,classified in different categories including cognitive science,computation,medicine,neurology,and philosophy,are reviewed.Based on this systematic review,desirable features of computationally aware systems(CASs)are gleaned and reported.A discussion about the relevance of computational awareness to advanced simulation is also outlined. 展开更多
关键词 Computational awareness computationally aware systems awareness consciousness computational understanding simulation
Large Language Model-Driven Evolutionary Optimization With a Hallucination-Resilient Mechanism 认领 引用
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作者 Weixiong Huang Rui Wang +3 位作者 Tao Zhang Sheng Qi Shichao Fan Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1428-1445,共18页
Large language models(LLMs)have demonstrated significant potential as black-box optimizers due to their strong reasoning capabilities.However,challenges such as the hallucination phenomenon introduce instability and u... Large language models(LLMs)have demonstrated significant potential as black-box optimizers due to their strong reasoning capabilities.However,challenges such as the hallucination phenomenon introduce instability and uncertainty,limiting their effectiveness.This paper proposes an LLM-driven evolutionary optimization framework,referred to as LLM-driven hybrid evolutionary optimization framework(LHO),that integrates LLMs with traditional evolutionary operators.LLMs accelerate the optimization process by generating high-quality solutions,while evolutionary operators ensure stability and provide performance guarantees.To further enhance robustness,we introduce a hallucination-resilient mechanism to mitigate the risks associated with LLM hallucinations.Experimental results on various benchmark tests,encompassing single-objective,multiobjective,and complex constrained multiobjective problems,confirm the effectiveness and practicality of the proposed framework,offering valuable insights and future directions for LLM as evolutionary optimizers. 展开更多
关键词 Evolutionary algorithm (EA) evolutionary computation large language models (LLMs)
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Risk-aware user satisfaction maximization in vehicle-assisted multi-access edge computing offloading:a game-theoretic approach 认领 引用
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作者 Yu Dai Jie Tian +2 位作者 Tiantian Li Jing Wang Chuanfen Feng 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期294-305,共12页
Multi-access Edge Computing(MEC)enhances computational efficiency by enabling resource-constrained User Devices(UD)to offload tasks to edge servers.Compared to traditional edge servers fixed on the Small Cellular Base... Multi-access Edge Computing(MEC)enhances computational efficiency by enabling resource-constrained User Devices(UD)to offload tasks to edge servers.Compared to traditional edge servers fixed on the Small Cellular Base Stations(SBS),mobile vehicles with idle resources serve as mobile edge servers,which can reduce UD's task latency due to closer proximity to the UD.However,due to the limited computation resources of vehicles and highly competitive among UD,the available computation resources provided by vehicles for UD are uncertain,which poses a challenge for UD in making task offloading decisions.In this paper,we establish a risk-aware task offloading framework in vehicle-assisted MEC networks with computation resource uncertainty,where UD make offloading decisions by considering their risk-aware behavior.We first characterize and model UD's riskaware behavior based on Prospect Theory(PT)and then formulate a user satisfaction maximization problem by optimizing the offloading strategy of UD.To solve it,we reformulate the above problem among multiple users as a non-cooperative game and prove the uniqueness of the Pure Nash Equilibrium(PNE).We also propose a lowcomplexity distributed iterative optimization algorithm to obtain the optimal offloading strategy.The simulation results demonstrate that the proposed scheme significantly enhances satisfaction utility of UD and reduces failure probability of vehicles compared to other benchmark methods. 展开更多
关键词 Task offloading Multi-access edge computing Vehicle-assisted Computation uncertainty Prospect theory Game theory
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Weighted k-NNC:An Efficient Computation Reduction Method for Metaheuristic-Based Structural Optimization 认领 引用
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作者 Anh-Vu Nguyen Tien-Chuong Vu +2 位作者 Ba-Duan Nguyen Hoang-Anh Pham Ravipudi Venkata Rao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期455-487,共33页
Structural optimization is essential for finding optimal designs in practical engineering tasks.Metaheuristic algorithms have been widely applied in structural optimization problems in recent years,especially when dea... Structural optimization is essential for finding optimal designs in practical engineering tasks.Metaheuristic algorithms have been widely applied in structural optimization problems in recent years,especially when dealing with discrete design variables,the nonlinearity of the objective function and constraints.Unlike gradientbased algorithms,which rely on the slope variation of a function,metaheuristic algorithms do not require derivative calculations and thus avoid being trapped in local optimum.However,metaheuristic algorithms often require numerous function evaluations,involving costly structural analyses,thus increasing computational load considerably.This paper investigates a method to reduce computational load,specifically by reducing the number of function evaluations for metaheuristic-based structural optimization problems.The proposed strategy is based on eliminating unpromising designs during the optimization process.For each newly generated solution,an early assessment through its k nearest neighbors,named k-nearest neighbor comparison(k-NNC),is applied,acting as a filter.If a solution is deemed less promising,it is eliminated without going through the function evaluation step.Conversely,if a solution is deemed good,it is retained for the next comparison and selection step.This paper presents the implementation sequence of k-NNC,highlighting its disadvantages in terms of efficiency and accuracy.From this,a new method,the distance-weighted k-nearest neighbor comparison(wkNNC),has been developed.In wkNNC,the distance from the k neighbors to the solution under consideration is used as the weight for comparison.Furthermore,an archive of infeasible solutions and the potential solution refinement are introduced for enhancing the accuracy and efficiency of wkNNC.The superiority of wkNNC is demonstrated in the sizing optimization of some benchmark discrete cross-section truss structures.The wkNNC method,combined with the Best-Worst-Random(BWR)algorithm,achieves a computational load reduction of over 80 percent. 展开更多
关键词 Structural optimization metaheuristic algorithm computation reduction k-NN distance-weighted k-NNC BWR algorithm
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Energy-Efficient Computation Offloading and Resource Allocation in Cloud-Edge Collaborative Computing Systems 认领 引用
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作者 Cheng Zhaoping Jiang Tao +3 位作者 Ke Chenxi Zhang Guoqiang Peng Miaoran Feng Mingjie 《China Communications》 SCIE EI CSCD 2026年第3期316-329,共14页
With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing sch... With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing schemes have not balanced energy efficiency and performance under edge node sleep control and still lack a joint optimization mechanism for computation offloading and resource allocation.This paper tackles the problem of optimizing energy-efficient computation offloading and resource allocation(CORA)in cloud-edge collaborative computing systems,where edge servers can dynamically enter sleep mode to reduce power consumption.We model the problem as a mixed-integer nonlinear programming formulation,with the objective of minimizing a weighted sum of overall task latency and the cumulative energy consumption of all IoT devices and edge servers.To handle the hybrid nature of discrete and continuous decision variables and the complex system dynamics,we reformulate the problem for each device as a Markov Decision Process and develop a deep deterministic policy gradient with multi-agent algorithm tailored for such hybrid action spaces.Simulation results show that the proposed CORA strategy achieves superior performance compared to three benchmark schemes with reduced latency and energy consumption. 展开更多
关键词 cloud-edge collaborative computing computation offloading deep reinforcement learning energy-efficient design
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Triphenylmethane-Derived Levelers for High-Speed Redistribution Layer Copper Electroplating of Tailored Surface Morphologies 认领 引用
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作者 Zi-Hao Song Wei-Bin Wang +10 位作者 Xiao-Hui Liu Xiao-Min Han Yi Zhou Rui Huang Yan-Xia Jiang Zhe Li Xiao-Wei Liu Mei-Ling Xiao Hong-Gang Liao Wei-Lin Xu Rong Sun 《电化学（中英文）》 CAS 北大核心 2026年第2期33-45,共13页
Redistribution Layer(RDL),composed of layered dielectrics and electroplated copper materials,is a basic structure to rearrange numerous I/O pads on the chip surface in wafer-level advanced packaging.As the key chemica... Redistribution Layer(RDL),composed of layered dielectrics and electroplated copper materials,is a basic structure to rearrange numerous I/O pads on the chip surface in wafer-level advanced packaging.As the key chemicals in electrolyte baths,electroplating additives have undergone continuous development to meet the industrial needs for high-speed and fine-line/fine-pitch applications.Meanwhile,the intricate relationships between additive chemical structures and electroplated copper properties are yet to be well understood.In this work,a pair of triphenylmethane-based dye molecules,i.e.,gentian violet(GV)and methyl green(MG),was comparatively investigated as levelers for high-speed RDL copper electroplating.Compared to GV,significantly stronger electrochemical polarization and tunable deposit morphology can be achieved by MG with just one extra quaternized amine terminal.Combining quantum chemical computations,in situ spectroelectrochemical analyses,and microstructural characterization,it is found that MG possesses enhanced electrostatic adsorption,surface coverage and multi-additive synergies,enabling tailored copper trace morphology.This study elaborates the adsorption mechanism and screening criteria of triphenylmethane-derived levelers,and presents a candidate additive structure for high-speed copper electroplating. 展开更多
关键词 Redistribution layer Copper electroplating leveler Theoretical computation In situ spectroelectrochemical analysis Microstructural characterization
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Multi-Objective Enhanced Cheetah Optimizer for Joint Optimization of Computation Offloading and Task Scheduling in Fog Computing 认领 引用
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作者 Ahmad Zia Nazia Azim +5 位作者 Bekarystankyzy Akbayan Khalid J.Alzahrani Ateeq Ur Rehman Faheem Ullah Khan Nouf Al-Kahtani Hend Khalid Alkahtani 《Computers, Materials & Continua》 SCIE EI 2026年第3期1559-1588,共30页
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c... The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods. 展开更多
关键词 Computation offloading task scheduling cheetah optimizer fog computing optimization resource allocation internet of things
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