Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta...Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Veh...For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.展开更多
This study develops an analytical model to evaluate the cooling performance of a porous terracotta tubular direct evaporative heat and mass exchanger. By combining energy and mass balance equations with heat and mass ...This study develops an analytical model to evaluate the cooling performance of a porous terracotta tubular direct evaporative heat and mass exchanger. By combining energy and mass balance equations with heat and mass transfer coefficients and air psychrometric correlations, the model provides insights into the impact of design and operational parameters on the exchanger cooling performance. Validated against an established numerical model, it accurately simulates cooling behavior with a Root Mean Square Deviation of 0.43 - 1.18˚C under varying inlet air conditions. The results show that tube geometry, including equivalent diameter, flatness ratio, and length significantly influences cooling outcomes. Smaller diameters enhance wet-bulb effectiveness but reduce cooling capacity, while increased flatness and length improve both. For example, extending the flatness ratio of a 15 mm diameter, 0.6 m long tube from 1 (circular) to 4 raises the exchange surface area from 0.028 to 0.037 m2, increasing wet-bulb effectiveness from 60% to 71%. Recommended diameters range from 5 mm for tubes under 0.5 m to 1 cm for tubes 0.5 to 1 m in length. Optimal air velocities depend on tube length: 1 m/s for tubes under 0.8 m, 1.5 m/s for lengths of 0.8 to 1.2 m, and up to 2 m/s for longer tubes. This model offers a practical alternative to complex numerical and CFD methods, with potential applications in cooling tower optimization for thermal and nuclear power plants and geothermal heat exchangers.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage...The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.展开更多
As a core power device in strategic industries such as new energy power generation and electric vehicles,the thermal reliability of IGBT modules directly determines the performance and lifetime of the whole system.A s...As a core power device in strategic industries such as new energy power generation and electric vehicles,the thermal reliability of IGBT modules directly determines the performance and lifetime of the whole system.A synergistic optimization structure of“inlet plate-channel spoiler columns”is proposed for the local hot spot problem during the operation of Insulated Gate Bipolar Transistor(IGBT),combined with the inherent defect of uneven flow distribution of the traditional U-type liquid cooling plate in this paper.The influences of the shape,height(H),and spacing from the spoiler column(b)of the plate on the comprehensive heat dissipation performance of the liquid cooling plate are analyzed at different Reynolds numbers,A dual heat source strategy is introduced and the effect of the optimized structure is evaluated by the temperature inhomogeneity coefficient(Φ).The results show that the optimum effect is achieved when the shape of the plate is square,H=4.5 mm,b=2 mm,and u=0.05 m/s,at which the HTPE=1.09 and Φ are reduced by 40%.In contrast,the maximum temperatures of the IGBT and the FWD(Free Wheeling Diode)chips are reduced by 8.7 and 8.4 K,respectively,and ΔP rises by only 1.58 Pa while keeping ΔT not significantly increased.This optimized configuration achieves a significant reduction in the critical chip temperature and optimization of the flow field uniformity with almost no change in the system flow resistance.It breaks through the limitation of single structure optimization of the traditional liquid cooling plate and effectively solves the problem of uneven flow in the U-shaped cooling plate,which provides a new solution with important engineering value for the thermal management of IGBT modules.展开更多
Double-wall effusion cooling coupled with thermal barrier coating(TBC)is an important way of thermal protection for gas turbine vanes and blades of next-generation aero-engine,and formation of discrete crater holes by...Double-wall effusion cooling coupled with thermal barrier coating(TBC)is an important way of thermal protection for gas turbine vanes and blades of next-generation aero-engine,and formation of discrete crater holes by TBC spraying is an approved design.To protect both metal and TBC synchronously,a recommended geometry of crater is obtained through a fully automatic multi-objective optimization combined with conjugate heat transfer simulation in this work.The length and width of crater(i.e.,L/D and W/D)were applied as design variables,and the area-averaged overall effectiveness of the metal and TBC surfaces(i.e.,Φ_(av) and τ_(av))were selected as objective functions.The optimization procedure consists of automated geometry and mesh generation,conjugate heat transfer simulation validated by experimental data and Kriging surrogated model.The results showed that the Φ_(av) and τ_(av) are successfully increased respectively by 9.1%and 6.0%through optimization.Appropriate enlargement of the width and length of the crater can significantly improve the film coverage effect,since that the beneficial anti-CRVP is enhanced and the harmful CRVP is weakened.展开更多
Power batteries serve as key components of new energy vehicles and are distinguished by their large capacity,long lifespan,high energy density,and stable operation.The strict temperature demands of power battery packs...Power batteries serve as key components of new energy vehicles and are distinguished by their large capacity,long lifespan,high energy density,and stable operation.The strict temperature demands of power battery packs necessitate the development of highly efficient thermal management systems.In this study,a converging-diverging liquid cooling channel featuring a wave shaped structure was designed and analyzed for 18,650-type lithium-ion batteries.To investigate the design methodology for flow channel structure,a thermal model for the heat generation rate of the 18,650-type battery was developed.A comparative analysis of four geometrical configurations of convergingdiverging channels.It identified the flat-bottomed channel achieves a maximum reduction of 20.6%in peak internal temperature compared to the other designs.Subsequently,the effects of the arc depth,cell spacing,and Reynolds number on the heat dissipation of the flat-bottomed flow channel were comprehensively investigated.The results demonstrated that increasing the Reynolds number,maximizing the arc depth of the converging-diverging structure,and reducing cell spacing considerably improved the cooling heat dissipation efficiency.Based on the particle swarm optimization algorithm,the optimal parameter combination of the battery pack was obtained at a discharge rate of 2C,comprising an arc depth of 8.5 mm,cell spacing of 1 mm,and Reynolds number of 700.The study provides valuable guidance and references for the practical design and implementation of thermal management systems in new energy vehicles.展开更多
Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the...Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the interplay among flood control,ecological integrity,and desilting objectives under varying watersediment conditions.The framework encompasses multi-objective reservoir optimal operation,scheme decision,and trade-off analysis among competing objectives.To address the optimization model,an elite mutation-based multiobjective particle swarm optimization(MOPSO)algorithm that integrates genetic algorithms(GA)is developed.The coupling coordination degree is employed for optimal scheme decision-making,allowing for the adjustment of weight ratios to investigate the trade-offs between objectives.This research focuses on the Sanmenxia and Xiaolangdi cascade reservoirs in the Yellow River,utilizing three representative hydrological years:1967,1969,and 2002.The findings reveal that:(1)the proposed model effectively generates Pareto fronts for multi-objective operations,facilitating the recommendation of optimal schemes based on coupling coordination degrees;(2)as water-sediment conditions shift from flooding to drought,competition intensifies between the flood control and desilting objectives.While flood control and ecological objectives compete during flood and dry years,they demonstrate synergies in normal years(r=0.22);conversely,ecological and desilting objectives are consistently competitive across all three typical years,with the strongest competition observed in the normal year(r=-0.95);(3)the advantages conferred to ecological objectives increase as water-sediment conditions shift from flooding to drought.However,the promotion of the desilting objective requires more complex trade-offs.This study provides a model and methodological approach for the multi-objective optimization of flood control,sediment management,and ecological considerations in reservoir clusters.Moreover,the methodologies presented herein can be extended to other water resource systems for multi-objective optimization and decision-making.展开更多
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
In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in ...In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.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
Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced...Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.展开更多
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje...To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
基金funded by National Natural Science Foundation of China(Nos.12402142,11832013 and 11572134)Natural Science Foundation of Hubei Province(No.2024AFB235)+1 种基金Hubei Provincial Department of Education Science and Technology Research Project(No.Q20221714)the Opening Foundation of Hubei Key Laboratory of Digital Textile Equipment(Nos.DTL2023019 and DTL2022012).
文摘Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金supported by the National Natural Science Foundation of China(No.62271399)the National Key Research and Development Program of China(No.2022YFB1807102)。
文摘For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.
文摘This study develops an analytical model to evaluate the cooling performance of a porous terracotta tubular direct evaporative heat and mass exchanger. By combining energy and mass balance equations with heat and mass transfer coefficients and air psychrometric correlations, the model provides insights into the impact of design and operational parameters on the exchanger cooling performance. Validated against an established numerical model, it accurately simulates cooling behavior with a Root Mean Square Deviation of 0.43 - 1.18˚C under varying inlet air conditions. The results show that tube geometry, including equivalent diameter, flatness ratio, and length significantly influences cooling outcomes. Smaller diameters enhance wet-bulb effectiveness but reduce cooling capacity, while increased flatness and length improve both. For example, extending the flatness ratio of a 15 mm diameter, 0.6 m long tube from 1 (circular) to 4 raises the exchange surface area from 0.028 to 0.037 m2, increasing wet-bulb effectiveness from 60% to 71%. Recommended diameters range from 5 mm for tubes under 0.5 m to 1 cm for tubes 0.5 to 1 m in length. Optimal air velocities depend on tube length: 1 m/s for tubes under 0.8 m, 1.5 m/s for lengths of 0.8 to 1.2 m, and up to 2 m/s for longer tubes. This model offers a practical alternative to complex numerical and CFD methods, with potential applications in cooling tower optimization for thermal and nuclear power plants and geothermal heat exchangers.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China(21YJA790009)National Natural Science Foundation of China(72140001).
文摘The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.
基金supported by Tianjin Science and Technology Planning Project(22YDTPJC0020).
文摘As a core power device in strategic industries such as new energy power generation and electric vehicles,the thermal reliability of IGBT modules directly determines the performance and lifetime of the whole system.A synergistic optimization structure of“inlet plate-channel spoiler columns”is proposed for the local hot spot problem during the operation of Insulated Gate Bipolar Transistor(IGBT),combined with the inherent defect of uneven flow distribution of the traditional U-type liquid cooling plate in this paper.The influences of the shape,height(H),and spacing from the spoiler column(b)of the plate on the comprehensive heat dissipation performance of the liquid cooling plate are analyzed at different Reynolds numbers,A dual heat source strategy is introduced and the effect of the optimized structure is evaluated by the temperature inhomogeneity coefficient(Φ).The results show that the optimum effect is achieved when the shape of the plate is square,H=4.5 mm,b=2 mm,and u=0.05 m/s,at which the HTPE=1.09 and Φ are reduced by 40%.In contrast,the maximum temperatures of the IGBT and the FWD(Free Wheeling Diode)chips are reduced by 8.7 and 8.4 K,respectively,and ΔP rises by only 1.58 Pa while keeping ΔT not significantly increased.This optimized configuration achieves a significant reduction in the critical chip temperature and optimization of the flow field uniformity with almost no change in the system flow resistance.It breaks through the limitation of single structure optimization of the traditional liquid cooling plate and effectively solves the problem of uneven flow in the U-shaped cooling plate,which provides a new solution with important engineering value for the thermal management of IGBT modules.
基金Anhui Provincial Natural Science Foundation of China(2108085ME176)the Natural Science Foundation of China(52276043)。
文摘Double-wall effusion cooling coupled with thermal barrier coating(TBC)is an important way of thermal protection for gas turbine vanes and blades of next-generation aero-engine,and formation of discrete crater holes by TBC spraying is an approved design.To protect both metal and TBC synchronously,a recommended geometry of crater is obtained through a fully automatic multi-objective optimization combined with conjugate heat transfer simulation in this work.The length and width of crater(i.e.,L/D and W/D)were applied as design variables,and the area-averaged overall effectiveness of the metal and TBC surfaces(i.e.,Φ_(av) and τ_(av))were selected as objective functions.The optimization procedure consists of automated geometry and mesh generation,conjugate heat transfer simulation validated by experimental data and Kriging surrogated model.The results showed that the Φ_(av) and τ_(av) are successfully increased respectively by 9.1%and 6.0%through optimization.Appropriate enlargement of the width and length of the crater can significantly improve the film coverage effect,since that the beneficial anti-CRVP is enhanced and the harmful CRVP is weakened.
基金funded by the National Nature Science Foundation of China,grant number 52406024.
文摘Power batteries serve as key components of new energy vehicles and are distinguished by their large capacity,long lifespan,high energy density,and stable operation.The strict temperature demands of power battery packs necessitate the development of highly efficient thermal management systems.In this study,a converging-diverging liquid cooling channel featuring a wave shaped structure was designed and analyzed for 18,650-type lithium-ion batteries.To investigate the design methodology for flow channel structure,a thermal model for the heat generation rate of the 18,650-type battery was developed.A comparative analysis of four geometrical configurations of convergingdiverging channels.It identified the flat-bottomed channel achieves a maximum reduction of 20.6%in peak internal temperature compared to the other designs.Subsequently,the effects of the arc depth,cell spacing,and Reynolds number on the heat dissipation of the flat-bottomed flow channel were comprehensively investigated.The results demonstrated that increasing the Reynolds number,maximizing the arc depth of the converging-diverging structure,and reducing cell spacing considerably improved the cooling heat dissipation efficiency.Based on the particle swarm optimization algorithm,the optimal parameter combination of the battery pack was obtained at a discharge rate of 2C,comprising an arc depth of 8.5 mm,cell spacing of 1 mm,and Reynolds number of 700.The study provides valuable guidance and references for the practical design and implementation of thermal management systems in new energy vehicles.
基金National Natural Science Foundation of China,Grant/Award Number:U2243228The Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention,Grant/Award Number:2022nkms04+1 种基金MOE(Ministry of Education in China)Liberal Arts and Social Sciences Foundation,Grant/Award Number:23YJCZH332Natural Science Foundation of Anhui Province,Grant/Award Numbers:2208085US03,2308085US13。
文摘Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the interplay among flood control,ecological integrity,and desilting objectives under varying watersediment conditions.The framework encompasses multi-objective reservoir optimal operation,scheme decision,and trade-off analysis among competing objectives.To address the optimization model,an elite mutation-based multiobjective particle swarm optimization(MOPSO)algorithm that integrates genetic algorithms(GA)is developed.The coupling coordination degree is employed for optimal scheme decision-making,allowing for the adjustment of weight ratios to investigate the trade-offs between objectives.This research focuses on the Sanmenxia and Xiaolangdi cascade reservoirs in the Yellow River,utilizing three representative hydrological years:1967,1969,and 2002.The findings reveal that:(1)the proposed model effectively generates Pareto fronts for multi-objective operations,facilitating the recommendation of optimal schemes based on coupling coordination degrees;(2)as water-sediment conditions shift from flooding to drought,competition intensifies between the flood control and desilting objectives.While flood control and ecological objectives compete during flood and dry years,they demonstrate synergies in normal years(r=0.22);conversely,ecological and desilting objectives are consistently competitive across all three typical years,with the strongest competition observed in the normal year(r=-0.95);(3)the advantages conferred to ecological objectives increase as water-sediment conditions shift from flooding to drought.However,the promotion of the desilting objective requires more complex trade-offs.This study provides a model and methodological approach for the multi-objective optimization of flood control,sediment management,and ecological considerations in reservoir clusters.Moreover,the methodologies presented herein can be extended to other water resource systems for multi-objective optimization and decision-making.
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National Natural Science Foundation of China,grant number 62205120,funded this research.
文摘In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.
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 Hundred-person Program of Chinese Academy of Sciences and the National Natural Science Foundation of China(No.11905074).
文摘Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.
基金Supported by State Grid Corporation of China Science and Technology Project:Research on Key Technologies for Intelligent Carbon Metrology in Vehicle-to-Grid Interaction(Project Number:B3018524000Q).
文摘To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
