This paper develops and analyzes a stochastic derivative-free optimization strategy.A key feature is the state-dependent adaptive variance.We prove global convergence in probability with algebraic rate and give the qu...This paper develops and analyzes a stochastic derivative-free optimization strategy.A key feature is the state-dependent adaptive variance.We prove global convergence in probability with algebraic rate and give the quantitative results in numerical examples.A striking fact is that convergence is achieved without explicit information of the gradient and even without comparing different objective function values as in established methods such as the simplex method and simulated annealing.It can otherwise be compared to annealing with state-dependent temperature.展开更多
We present a new derivative-free optimization algorithm based on the sparse grid numerical integration. The algorithm applies to a smooth nonlinear objective function where calculating its gradient is impossible and e...We present a new derivative-free optimization algorithm based on the sparse grid numerical integration. The algorithm applies to a smooth nonlinear objective function where calculating its gradient is impossible and evaluating its value is also very expensive. The new algorithm has: 1) a unique starting point strategy;2) an effective global search heuristic;and 3) consistent local convergence. These are achieved through a uniform use of sparse grid numerical integration. Numerical experiment result indicates that the algorithm is accurate and efficient, and benchmarks favourably against several state-of-art derivative free algorithms.展开更多
Derivative-free optimization(DFO)problems are optimization problems where the derivative information is unavailable.The least Frobenius norm updating quadratic interpolation model function is one of the essential unde...Derivative-free optimization(DFO)problems are optimization problems where the derivative information is unavailable.The least Frobenius norm updating quadratic interpolation model function is one of the essential under-determined model functions for model-based derivative-free trust-region methods.This article proposes derivative-free optimization with transformed objective functions(DFOTO)and gives a model-based trust-region method with the least Frobenius norm model.The model updating formula is based on Powell’s formula and can be easily implemented.The method shares the same framework with those for problems without transformations,and its query scheme is given.We propose the definitions related to optimality-preserving transformations to understand the interpolation model in our method when minimizing transformed objective functions.We prove the existence of model optimality-preserving transformations beyond translation transformations.The necessary and sufficient condition for such transformations is given.An interesting discovery is that,as a fundamental transformation,the affine transformation with a(non-trivial)positive multiplication coefficient is not model optimality-preserving.We also analyze the corresponding least Frobenius norm updating model and its interpolation error when the objective function is affinely transformed.The convergence property of a provable algorithmic framework containing the least Frobenius norm updating quadratic model for minimizing transformed objective functions is given.Numerical results show that our method can successfully solve most test problems with objective optimality-preserving transformations,even though some of such transformations will change the optimality of the model function.To our best knowledge,this is the first work providing the model-based derivative-free algorithm and analysis for transformed problems with the function evaluation oracle.This article also proposes the“moving-target”optimization problem as an open problem.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
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
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightw...Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.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
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
Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft ...Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft flight at conventional rotational speeds in the Martian atmosphere.In this paper,we employ the Particle Swarm Optimization(PSO)algorithm to search for the control points of the Bezier curve,completing the parameterization of the airfoil upper and lower curves based on these control points.In order to directly enhance the lift-to-drag ratio of the airfoil at high angles of attack,the NSGA-II algorithm is utilized to optimize the lift-to-drag ratio of NACA 6904 at a=17.5°,Ma=0.43,Re=7600,and CLF 5605 at a=15°,Ma=0.7,Re=7481,respectively.The two-dimensional RANS(Reynolds Average NavierStokes)and k-ωSST turbulence models are employed in the optimization process by CFD to predict the lift and drag characteristics of the airfoil in a Martian environment.Under simulated Mars atmospheric conditions(pressure of 1380 Pa,test temperature of 24°C,equivalent Mars atmospheric density at the surface of 0.0162 g/cm~3),the airfoil after optimized is subjected to rotor lift-drag characteristic tests where a single-rotor lift-drag characteristic test bench is employed for verification.The experimental results demonstrate that the RB-TB-II blade,which is obtained by optimizing the airfoil based on the RB-SWQ-I blade,exhibits a 19.6%increase in Power Loading(PL)and a 20.4%increase in Figure of Merit(FM)compared with the RB-SWQ-I blade.Based on the results of airfoil optimization,increasing the camber at the leading edge of the airfoil under high angles of attack contributes to an improved lift-to-drag ratio.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
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative...In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.展开更多
Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a nove...Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.展开更多
Circumlunar abort trajectories constitute a vital contingency return strategy during the translunar phase of crewed lunar missions.This paper proposes a methodology for constructing the solution set of the circumlunar...Circumlunar abort trajectories constitute a vital contingency return strategy during the translunar phase of crewed lunar missions.This paper proposes a methodology for constructing the solution set of the circumlunar abort trajectory and leverages its advantageous properties to address the optimization design problem of abort trajectories.Initially,a solution set of all feasible abort trajectories,originating from an abort point on the nominal trajectory and complying with fundamental reentry constraints,is formulated through the introduction of two novel design parameters.Subsequently,the geometric characteristics of the solution set,as well as the distributional properties of key iterative constraint responses,including flight time and velocity increment,are analyzed.Finally,the characteristics exhibited in the solution set are employed to directly identify the design parameters of the abort trajectories with minimum flight time and velocity increment,thereby providing solutions to two distinct types of optimization problems.The simulation results for a variety of nominal trajectories,encompassing the reconstruction and redesign of the Apollo13 abort trajectory,validate the proposed method,demonstrating its ability to directly generate optimal abort trajectories.The method proposed in this paper investigates feasible abort trajectories from a global perspective,providing both a framework and convenience for mission planning and iterative optimization in abort trajectory design.展开更多
In this paper,we consider a robust semi-infinite interval-valued optimization problem with inequality constraints having an uncertain parameter.The parametric representation of the aforesaid problem is also considered...In this paper,we consider a robust semi-infinite interval-valued optimization problem with inequality constraints having an uncertain parameter.The parametric representation of the aforesaid problem is also considered in order to derive the necessary and sufficient optimality conditions.Furthermore,we formulate a mixed-type dual problem and derive duality results which associate the robust weak efficient solution of the primal and its dual problems.Several examples are given to illustrate the results in the manuscript.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 stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimi...The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.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partially supported by the National Science Foundation through grants DMS-2208504(BE),DMS-1913309(KR),DMS-1937254(KR),and DMS-1913129(YY)support from Dr.Max Rossler,the Walter Haefner Foundation,and the ETH Zurich Foundation.
文摘This paper develops and analyzes a stochastic derivative-free optimization strategy.A key feature is the state-dependent adaptive variance.We prove global convergence in probability with algebraic rate and give the quantitative results in numerical examples.A striking fact is that convergence is achieved without explicit information of the gradient and even without comparing different objective function values as in established methods such as the simplex method and simulated annealing.It can otherwise be compared to annealing with state-dependent temperature.
文摘We present a new derivative-free optimization algorithm based on the sparse grid numerical integration. The algorithm applies to a smooth nonlinear objective function where calculating its gradient is impossible and evaluating its value is also very expensive. The new algorithm has: 1) a unique starting point strategy;2) an effective global search heuristic;and 3) consistent local convergence. These are achieved through a uniform use of sparse grid numerical integration. Numerical experiment result indicates that the algorithm is accurate and efficient, and benchmarks favourably against several state-of-art derivative free algorithms.
基金the National Natural Science Foundation of China(No.12288201)。
文摘Derivative-free optimization(DFO)problems are optimization problems where the derivative information is unavailable.The least Frobenius norm updating quadratic interpolation model function is one of the essential under-determined model functions for model-based derivative-free trust-region methods.This article proposes derivative-free optimization with transformed objective functions(DFOTO)and gives a model-based trust-region method with the least Frobenius norm model.The model updating formula is based on Powell’s formula and can be easily implemented.The method shares the same framework with those for problems without transformations,and its query scheme is given.We propose the definitions related to optimality-preserving transformations to understand the interpolation model in our method when minimizing transformed objective functions.We prove the existence of model optimality-preserving transformations beyond translation transformations.The necessary and sufficient condition for such transformations is given.An interesting discovery is that,as a fundamental transformation,the affine transformation with a(non-trivial)positive multiplication coefficient is not model optimality-preserving.We also analyze the corresponding least Frobenius norm updating model and its interpolation error when the objective function is affinely transformed.The convergence property of a provable algorithmic framework containing the least Frobenius norm updating quadratic model for minimizing transformed objective functions is given.Numerical results show that our method can successfully solve most test problems with objective optimality-preserving transformations,even though some of such transformations will change the optimality of the model function.To our best knowledge,this is the first work providing the model-based derivative-free algorithm and analysis for transformed problems with the function evaluation oracle.This article also proposes the“moving-target”optimization problem as an open problem.
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 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.
基金Supported by National Natural Science Foundation of China(Grant Nos.52202431,52172353).
文摘Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.
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(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.
基金supported by the National Key R&D Program of China(No.2024YFC3015804)the Basic Science Center Program for“Space Robot Intelligent Manipulation”,China(No.T2388101)。
文摘Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft flight at conventional rotational speeds in the Martian atmosphere.In this paper,we employ the Particle Swarm Optimization(PSO)algorithm to search for the control points of the Bezier curve,completing the parameterization of the airfoil upper and lower curves based on these control points.In order to directly enhance the lift-to-drag ratio of the airfoil at high angles of attack,the NSGA-II algorithm is utilized to optimize the lift-to-drag ratio of NACA 6904 at a=17.5°,Ma=0.43,Re=7600,and CLF 5605 at a=15°,Ma=0.7,Re=7481,respectively.The two-dimensional RANS(Reynolds Average NavierStokes)and k-ωSST turbulence models are employed in the optimization process by CFD to predict the lift and drag characteristics of the airfoil in a Martian environment.Under simulated Mars atmospheric conditions(pressure of 1380 Pa,test temperature of 24°C,equivalent Mars atmospheric density at the surface of 0.0162 g/cm~3),the airfoil after optimized is subjected to rotor lift-drag characteristic tests where a single-rotor lift-drag characteristic test bench is employed for verification.The experimental results demonstrate that the RB-TB-II blade,which is obtained by optimizing the airfoil based on the RB-SWQ-I blade,exhibits a 19.6%increase in Power Loading(PL)and a 20.4%increase in Figure of Merit(FM)compared with the RB-SWQ-I blade.Based on the results of airfoil optimization,increasing the camber at the leading edge of the airfoil under high angles of attack contributes to an improved lift-to-drag ratio.
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.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
文摘In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.
基金co-supported by the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(No.22kftk01)the Key Research and Development Program of Heilongjiang,China(No.2024ZXJ07B05)the National Natural Science Foundation of China(No.92471103)。
文摘Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.
文摘Circumlunar abort trajectories constitute a vital contingency return strategy during the translunar phase of crewed lunar missions.This paper proposes a methodology for constructing the solution set of the circumlunar abort trajectory and leverages its advantageous properties to address the optimization design problem of abort trajectories.Initially,a solution set of all feasible abort trajectories,originating from an abort point on the nominal trajectory and complying with fundamental reentry constraints,is formulated through the introduction of two novel design parameters.Subsequently,the geometric characteristics of the solution set,as well as the distributional properties of key iterative constraint responses,including flight time and velocity increment,are analyzed.Finally,the characteristics exhibited in the solution set are employed to directly identify the design parameters of the abort trajectories with minimum flight time and velocity increment,thereby providing solutions to two distinct types of optimization problems.The simulation results for a variety of nominal trajectories,encompassing the reconstruction and redesign of the Apollo13 abort trajectory,validate the proposed method,demonstrating its ability to directly generate optimal abort trajectories.The method proposed in this paper investigates feasible abort trajectories from a global perspective,providing both a framework and convenience for mission planning and iterative optimization in abort trajectory design.
基金supported by the MATRICES,SERB-DST,New Delhi,India(No.MTR/2021/000002).
文摘In this paper,we consider a robust semi-infinite interval-valued optimization problem with inequality constraints having an uncertain parameter.The parametric representation of the aforesaid problem is also considered in order to derive the necessary and sufficient optimality conditions.Furthermore,we formulate a mixed-type dual problem and derive duality results which associate the robust weak efficient solution of the primal and its dual problems.Several examples are given to illustrate the results in the manuscript.
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Project supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2025-02303676)。
文摘The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.
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.
