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
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.展开更多
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
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
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
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
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
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
The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are ...The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.展开更多
This article proposes an innovative adversarial attack method,AMA(Adaptive Multimodal Attack),which introduces an adaptive feedback mechanism by dynamically adjusting the perturbation strength.Specifically,AMA adjusts...This article proposes an innovative adversarial attack method,AMA(Adaptive Multimodal Attack),which introduces an adaptive feedback mechanism by dynamically adjusting the perturbation strength.Specifically,AMA adjusts perturbation amplitude based on task complexity and optimizes the perturbation direction based on the gradient direction in real time to enhance attack efficiency.Experimental results demonstrate that AMA elevates attack success rates from approximately 78.95%to 89.56%on visual question answering and from78.82%to 84.96%on visual reasoning tasks across representative vision-language benchmarks.These findings demonstrate AMA’s superior attack efficiency and reveal the vulnerability of current visual language models to carefully crafted adversarial examples,underscoring the need to enhance their robustness.展开更多
This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of...The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of thrust.Therefore,this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition.Firstly,an ACE component model with inlet flow characteristics was established,and the performance before and after mode transition were analyzed.Secondly,the principle of ACE acceleration optimization was analyzed,and the Front Variable Area Bypass Injector(FVABI)and Mode Selection Valve(MSV)were adopted in the acceleration process.Finally,based on the Sequential Quadratic Programming(SQP)algorithm,considering the degradation effects of engine components,we optimize the acceleration control plan for fuel and variable geometry mechanisms.The simulation results show that at the subsonic cruise point,the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%,and accelerate the thrust response speed by 33.72%.When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%,the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%;within the flight envelope,the acceleration time is reduced by more than 25%,and the thrust response speed is increased by more than 20%.展开更多
The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are c...The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.展开更多
This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to mai...This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to maintain proximity to their mobile physical twin(PT)counterparts.To minimize task response latency under a stringent energy consumption constraint,we jointly optimize three key components:the status data uploading frequency fromthe PT,theDT migration decisions,and the allocation of computational and communication resources.To address the asynchronous nature of these decisions,we propose a novel two-timescale mobility-aware online optimization(TMO)framework.The TMO scheme leverages an extended two-timescale Lyapunov optimization framework to decompose the long-term problem into sequential subproblems.At the larger timescale,a multi-armed bandit(MAB)algorithm is employed to dynamically learn the optimal status data uploading frequency.Within each shorter timescale,we first employ a gated recurrent unit(GRU)-based predictor to forecast the PT’s trajectory.Based on this prediction,an alternate minimization(AM)algorithm is then utilized to solve for the DT migration and resource allocation variables.Theoretical analysis confirms that the proposed TMO scheme is asymptotically optimal.Furthermore,simulation results demonstrate its significant performance gains over existing benchmark methods.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying...The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multimodal data fusion.This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large languagemodels(LLMs)with adaptive optimization,achieving three key innovations:(1)Ahierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction,(2)Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds(Daubechies-4 basis,6-level decomposition),and(3)A grouping-stratified ant colony optimization algorithm featuring penalty-based pheromone updating.Validated on IEEE 33/100-node systems,our framework demonstrates 96.7%fault localization accuracy(23%improvement over STGCN)and 0.82-s protection delay,outperforming MILP-basedmethods by 37%in reconfiguration speed.The system maintains 98.4%self-healing success rate under cascading faults,resolving 89.3%of phase-toground faults within 500 ms through adaptive impedance matching.Field tests on 220 kV substations with 45%renewable penetration show 99.1%voltage stability(±5%deviation threshold)and 40%communication efficiency gains via compressed GOOSE message parsing.Comparative analysis reveals 12.6×faster convergence than conventional ACO in 1000-node networks,with 95.2%robustness against±25%load fluctuations.These advancements provide a scalable solution for real-time fault recovery in renewable-dense grids,reducing outage duration by 63%inmulti-agent simulations compared to centralized architectures.展开更多
Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durabili...Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(...Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘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.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
文摘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.
文摘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.
基金Supported by National Natural Science Foundation of China(Grant No.52105271).
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.12372200 and 12072242).
文摘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.
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘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.
基金supported by the National Natural Science Foundation of China[Grant No.12371378]the Fujian Natural Science Foundation[Grant No.2022J01378,Grant No.2023J011127].
文摘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%.
基金supported by the Scientific Research Foundation of Nanjing Institute of Technology(No.YKJ202425)the National Natural Science Foundation of China(No.72301130).
文摘The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.
基金funded by the Natural Science Foundation of Jiangsu Province(Program BK20240699)National Natural Science Foundation of China(Program 62402228).
文摘This article proposes an innovative adversarial attack method,AMA(Adaptive Multimodal Attack),which introduces an adaptive feedback mechanism by dynamically adjusting the perturbation strength.Specifically,AMA adjusts perturbation amplitude based on task complexity and optimizes the perturbation direction based on the gradient direction in real time to enhance attack efficiency.Experimental results demonstrate that AMA elevates attack success rates from approximately 78.95%to 89.56%on visual question answering and from78.82%to 84.96%on visual reasoning tasks across representative vision-language benchmarks.These findings demonstrate AMA’s superior attack efficiency and reveal the vulnerability of current visual language models to carefully crafted adversarial examples,underscoring the need to enhance their robustness.
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
基金supported in part by the National Natural Science Foundation of China(No.52372389)the Jiangsu Province Excellent Postdoctoral Program of China(No.2023ZB494)+1 种基金the Basic Research Program of Jiangsu Province,China(No.BK20241412)the National Science Foundation for Post-doctoral Scientists of China(No.2024M754131)。
文摘The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of thrust.Therefore,this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition.Firstly,an ACE component model with inlet flow characteristics was established,and the performance before and after mode transition were analyzed.Secondly,the principle of ACE acceleration optimization was analyzed,and the Front Variable Area Bypass Injector(FVABI)and Mode Selection Valve(MSV)were adopted in the acceleration process.Finally,based on the Sequential Quadratic Programming(SQP)algorithm,considering the degradation effects of engine components,we optimize the acceleration control plan for fuel and variable geometry mechanisms.The simulation results show that at the subsonic cruise point,the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%,and accelerate the thrust response speed by 33.72%.When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%,the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%;within the flight envelope,the acceleration time is reduced by more than 25%,and the thrust response speed is increased by more than 20%.
文摘The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.
基金funded by the State Key Laboratory of Massive Personalized Customization System and Technology,grant No.H&C-MPC-2023-04-01.
文摘This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to maintain proximity to their mobile physical twin(PT)counterparts.To minimize task response latency under a stringent energy consumption constraint,we jointly optimize three key components:the status data uploading frequency fromthe PT,theDT migration decisions,and the allocation of computational and communication resources.To address the asynchronous nature of these decisions,we propose a novel two-timescale mobility-aware online optimization(TMO)framework.The TMO scheme leverages an extended two-timescale Lyapunov optimization framework to decompose the long-term problem into sequential subproblems.At the larger timescale,a multi-armed bandit(MAB)algorithm is employed to dynamically learn the optimal status data uploading frequency.Within each shorter timescale,we first employ a gated recurrent unit(GRU)-based predictor to forecast the PT’s trajectory.Based on this prediction,an alternate minimization(AM)algorithm is then utilized to solve for the DT migration and resource allocation variables.Theoretical analysis confirms that the proposed TMO scheme is asymptotically optimal.Furthermore,simulation results demonstrate its significant performance gains over existing benchmark methods.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
基金the project“Research on Power SafetyDecision Support SystemBased on Large Language Models”(Science and Technology Project of Huaian Hongneng Group Co.,Ltd.)under Contract No.SGTYHT/23-JS-001.
文摘The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multimodal data fusion.This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large languagemodels(LLMs)with adaptive optimization,achieving three key innovations:(1)Ahierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction,(2)Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds(Daubechies-4 basis,6-level decomposition),and(3)A grouping-stratified ant colony optimization algorithm featuring penalty-based pheromone updating.Validated on IEEE 33/100-node systems,our framework demonstrates 96.7%fault localization accuracy(23%improvement over STGCN)and 0.82-s protection delay,outperforming MILP-basedmethods by 37%in reconfiguration speed.The system maintains 98.4%self-healing success rate under cascading faults,resolving 89.3%of phase-toground faults within 500 ms through adaptive impedance matching.Field tests on 220 kV substations with 45%renewable penetration show 99.1%voltage stability(±5%deviation threshold)and 40%communication efficiency gains via compressed GOOSE message parsing.Comparative analysis reveals 12.6×faster convergence than conventional ACO in 1000-node networks,with 95.2%robustness against±25%load fluctuations.These advancements provide a scalable solution for real-time fault recovery in renewable-dense grids,reducing outage duration by 63%inmulti-agent simulations compared to centralized architectures.
基金National MCF Energy R&D Program(2024YFE03260300)。
文摘Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金supported by the National Natural Science Foundation of China(Grant No.12402139,No.52368070)supported by Hainan Provincial Natural Science Foundation of China(Grant No.524QN223)+3 种基金Scientific Research Startup Foundation of Hainan University(Grant No.RZ2300002710)State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ24107)the Horizontal Research Project(Grant No.HD-KYH-2024022)Innovative Research Projects for Postgraduate Students in Hainan Province(Grant No.Hys2025-217).
文摘Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
