Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc...Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.展开更多
Multiple tuned mass dampers(MTMDs)reduce dynamic response with multiple specified frequencies of building structures.Many optimization algorithms for placement design exist,though they rarely conform to code-based ver...Multiple tuned mass dampers(MTMDs)reduce dynamic response with multiple specified frequencies of building structures.Many optimization algorithms for placement design exist,though they rarely conform to code-based verification nor produce high quality solutions without high computational effort and high complexity.This study proposes an inverse element exchange method(IEEM)with multi-level programming and compares it to a single tuned mass damper(STMD)and uniform distribution of multiple tuned mass dampers in the frequency and time domains.A ten-story shear building is used for the numerical case study.The results show that the proposed method can offer improvement over the STMD,uniform distribution of multiple tuned mass dampers,and distribution optimized by genetic algorithms(GA)with regard to minimizing the interstory drift ratio(IDR)in both the frequency and time domains and the time consumption for optimization.展开更多
Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service lif...Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.展开更多
It is a challenging issue to obtain the minimum amplitude control for linear systems subject to amplitudebounded disturbances.The difficulty is how to accurately give the quantitative relationship between the system H...It is a challenging issue to obtain the minimum amplitude control for linear systems subject to amplitudebounded disturbances.The difficulty is how to accurately give the quantitative relationship between the system H∞norm and control parameters.An optimal-Lyapunov-function-based controller design concept is proposed,and a minimum amplitude control scheme is presented under amplitude-bounded disturbances.Firstly,the optimal Lyapunov function is proposed by analyzing the geometric characteristics of the system H∞norm,and the necessary and sufficient condition of the optimal Lyapunov function parameter matrix is given.Secondly,the optimal Lyapunov function parameter matrix is constructed in the parameterized matrix equation,and the accurate quantitative relationship between the system H∞norm and control parameters is given.Finally,the control parameter optimization method is proposed according to the quantitative relationship between the system H∞norm and control parameters.Unlike robust optimization control methods,the presented minimum amplitude control scheme avoids the improper selection of the Lyapunov function in the controller design,and provides a novel way to design the minimum amplitude control under the given control accuracy.A buck converter example is given to illustrate the effectiveness and practicability of the presented scheme.展开更多
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,...Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach.展开更多
Functionally graded cellular structures(FGCSs)have a multitude of applications to a wide range of industries.Utilising the ever-progressing technology of additive manufacturing(AM),FGCSs can be applied to control mate...Functionally graded cellular structures(FGCSs)have a multitude of applications to a wide range of industries.Utilising the ever-progressing technology of additive manufacturing(AM),FGCSs can be applied to control material grading and achieve the desired mechanical properties.The current study explores the design and optimisation of FGCSs for AM,with a focus on improving the compression and impact performance of below knee(BK)prosthetic limbs made of thermoplastic polyurethane(TPU).A multiscale research methodology integrating topology optimization(TO),finite element analysis(FEA),and design of experiments(Do E)was adopted to optimise lattice structures in terms of stiffness and lightweight properties.Two-unit cell designs were considered in the study:Schwarz P gyroid and body-centered cubic(BCC).Response surface methodology(RSM)was implemented to analyse the effect of minimum and maximum cell wall thickness,cell size,and unit cell type on the mechanical performance of TPU FGCS structures.The results indicated that a Schwarz P FGCS structure with cell size,minimum and maximum cell wall thickness of 6,0.9 and 2.8 mm,respectively,could be optimal for a compromise between performance and weight.In this optimized case,stiffness and volume fraction values of 684 N/mm and 0.64 were obtained,respectively.The study also presents a proof-of-concept design for a BK prosthetic damper,highlighting the potential of FGCSs to enhance patient comfort,reduce manufacturing costs,and enable personalised designs through 3D scanning and AM.The obtained results could be a step forward towards the incorporation of AM technologies in prosthetics,offering a pathway to lightweight,cost-effective,and functionally tailored solutions.展开更多
With the escalating demand for safe,sustainable,and high-performance energy storage systems,hydrogel electrolytes have emerged as promising alternatives to conventional liquid electrolytes in zinc-ion batteries.By int...With the escalating demand for safe,sustainable,and high-performance energy storage systems,hydrogel electrolytes have emerged as promising alternatives to conventional liquid electrolytes in zinc-ion batteries.By integrating the high ionic conductivity of liquid electrolytes with the mechanical robustness of solid frameworks,hydrogel electrolytes offer distinct advantages in suppressing zinc dendrite formation,enhancing interfacial stability,and enabling reliable operation under extreme environmental conditions.This review systematically summarizes the fundamental characteristics and design criteria of hydrogel electrolytes,including mechanical flexibility,ionic transport capabilities,and environmental adaptability.It further explores various compositional design strategies involving natural polymers,synthetic polymers,and composite systems,as well as the incorporation of electrolyte salts and functional additives.In addition,recent advances in functional optimization,such as anti-freezing properties,self-healing abilities,thermal responsiveness,and biocompatibility,are comprehensively discussed.Finally,the review outlines the current challenges and proposes potential directions for future research.展开更多
In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperati...In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperative behavior of players in the system and introduce the concept of positive-incentive noise in CAID.We design a global cost function to ensure convergence of the system can be achieved and strive to improve the final level of cooperation.An optimal CAID control method is proposed to derive the deterministic optimal learning rate in analytical form,avoiding the variability and uncertainty brought about by neural network fitting or parameter adjustment.On this basis,the convergence of the dynamic model is further analyzed by using the Lyapunov function instead of the Jacobian matrix.Additionally,an adaptive filtering mechanism is designed to dynamically ensure that only positive-incentive noise affects the system,effectively reducing the impact of negative noise and enhancing system stability.The framework is validated through simulations involving triple classical game models,including the hawk-dove game,the stag hunt game,the chicken game on networks,and a straightforward illustrative example.展开更多
Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse ...Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse of warm dense matter theory is thermal density functional theory(DFT),which,however,suffers from two limitations:(i)its accuracy can depend on the utilized exchange-correlation functional,which has to be approximated,and(ii)it is generally limited to single-electron properties such as the density distribution.Here,we present a new ansatz combining time-dependent DFT results for the dynamic structure factor S_(ee)(q,ω)with static DFT results for the density response.This allows us to estimate the electron-electron static structure factor S_(ee)(q)of warm dense hydrogen with high accuracy over a broad range of densities and temperatures.In addition to its value for the study of warm dense matter,our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.展开更多
Multi</span><span><span style="font-family:"">-</span></span><span><span style="font-family:"">goal and multi-objective optimizations are similar...Multi</span><span><span style="font-family:"">-</span></span><span><span style="font-family:"">goal and multi-objective optimizations are similar techniques to</span></span><span><span style="font-family:""> achieve <span>multiple conflicting goals/objectives simultaneously. There are several tech</span>niques <span>for solving multi-goal and multi-objective optimization problems. The</span> <span>present </span><span>study proposed the possibility of convertibility in solving multi-goal and mul</span>ti-objective optimization problems.展开更多
A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good...A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good performance. The computation results show that its generality, precision, robustness, simplicity and performance are all satisfactory.展开更多
An adaptive immune-genetic algorithm （AIGA） is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response （secondary response）, adaptive mutation and density opera...An adaptive immune-genetic algorithm （AIGA） is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response （secondary response）, adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.展开更多
As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully ...As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function（SEO-SRBF）. Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of thematerial volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.展开更多
This paper presents a two-phase genetic algorithm (TPGA) based on the multi- parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population' s evol vement to diversity or convergence accor...This paper presents a two-phase genetic algorithm (TPGA) based on the multi- parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population' s evol vement to diversity or convergence according to the population size and the crossover size, so we make it run in different forms during the global and local optimization phases and then forms TPGA. The experiment results show that TPGA is very efficient for the optimization of low-dimension multi-modal functions, usually we can obtain all the global optimal solutions.展开更多
The filled function method is an approach for finding a global minimum of multi-dimensional functions. With more and more relevant research, it becomes a promising way used in unconstrained global optimization. Some f...The filled function method is an approach for finding a global minimum of multi-dimensional functions. With more and more relevant research, it becomes a promising way used in unconstrained global optimization. Some filled functions with one or two parameters have already been suggested. However, there is no certain criterion to choose a parameter appropriately. In this paper, a parameter-free filled function was proposed. The definition of the original filled function and assumptions of the objective function given by Ge were improved according to the presented parameter-free filled function. The algorithm and numerical results of test functions were reported. Conclusions were drawn in the end. Key words global optimization - filled function method - local minimizer MSC 2000 90C30展开更多
Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collabora...Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.展开更多
Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, impr...Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.展开更多
This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global sea...This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.展开更多
In this study,2~5 （five factors at two-level factorial design） design of experiment was applied to investigate a set of optimal machining parameters to achieve a minimum surface roughness value for Abies nordmannian...In this study,2~5 （five factors at two-level factorial design） design of experiment was applied to investigate a set of optimal machining parameters to achieve a minimum surface roughness value for Abies nordmanniana.Wood specimens were prepared using different values of spindle speed,feed rate,depth of cut,tool radius,and cutting directions.Average surface roughness （R_z） values were applied using a stylus.The objectives were to：（1）obtain the effective variables of wood surface roughness;（2） analyze which of these factors had an impact on variability in the CNC machining process;（3） evaluate the optimal cutting values within the range of different cutting levels of machining parameters.The results indicate that the design of experiment（DOE） based on the desirability function approach determined the optimal machining parameters successfully,leading to minimum R_a compared to the observed value.Minimum surface roughness values of tangential and radial cutting directions were 3.58 and 3.21 μm,respectively.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under Grant number:82171965.
文摘Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.
文摘Multiple tuned mass dampers(MTMDs)reduce dynamic response with multiple specified frequencies of building structures.Many optimization algorithms for placement design exist,though they rarely conform to code-based verification nor produce high quality solutions without high computational effort and high complexity.This study proposes an inverse element exchange method(IEEM)with multi-level programming and compares it to a single tuned mass damper(STMD)and uniform distribution of multiple tuned mass dampers in the frequency and time domains.A ten-story shear building is used for the numerical case study.The results show that the proposed method can offer improvement over the STMD,uniform distribution of multiple tuned mass dampers,and distribution optimized by genetic algorithms(GA)with regard to minimizing the interstory drift ratio(IDR)in both the frequency and time domains and the time consumption for optimization.
基金fundings supported by Sichuan Science and Technology Program(2025YFHZ0065).
文摘Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.
基金supported in part by the National Natural Science Foundation of China(62373089).
文摘It is a challenging issue to obtain the minimum amplitude control for linear systems subject to amplitudebounded disturbances.The difficulty is how to accurately give the quantitative relationship between the system H∞norm and control parameters.An optimal-Lyapunov-function-based controller design concept is proposed,and a minimum amplitude control scheme is presented under amplitude-bounded disturbances.Firstly,the optimal Lyapunov function is proposed by analyzing the geometric characteristics of the system H∞norm,and the necessary and sufficient condition of the optimal Lyapunov function parameter matrix is given.Secondly,the optimal Lyapunov function parameter matrix is constructed in the parameterized matrix equation,and the accurate quantitative relationship between the system H∞norm and control parameters is given.Finally,the control parameter optimization method is proposed according to the quantitative relationship between the system H∞norm and control parameters.Unlike robust optimization control methods,the presented minimum amplitude control scheme avoids the improper selection of the Lyapunov function in the controller design,and provides a novel way to design the minimum amplitude control under the given control accuracy.A buck converter example is given to illustrate the effectiveness and practicability of the presented scheme.
基金supported by National Key Research and Development Program of China under Grant 2024YFE0210800National Natural Science Foundation of China under Grant 62495062Beijing Natural Science Foundation under Grant L242017.
文摘The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.
基金supported by National Key Research and Development Program of China(2024YFE0214000)National Natural Science Foundation of China(62173308)+3 种基金Natural Science Foundation of Zhejiang Province of China(LRG25F030002)Zhejiang Province Leading Geese Plan(2025C01056)Jinhua Science and Technology Project(2022-1-042)Natural Science Foundation of Jiangsu Province(BK20240009).
文摘Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach.
基金financially supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(No.IMSIU-DDRSP2503)。
文摘Functionally graded cellular structures(FGCSs)have a multitude of applications to a wide range of industries.Utilising the ever-progressing technology of additive manufacturing(AM),FGCSs can be applied to control material grading and achieve the desired mechanical properties.The current study explores the design and optimisation of FGCSs for AM,with a focus on improving the compression and impact performance of below knee(BK)prosthetic limbs made of thermoplastic polyurethane(TPU).A multiscale research methodology integrating topology optimization(TO),finite element analysis(FEA),and design of experiments(Do E)was adopted to optimise lattice structures in terms of stiffness and lightweight properties.Two-unit cell designs were considered in the study:Schwarz P gyroid and body-centered cubic(BCC).Response surface methodology(RSM)was implemented to analyse the effect of minimum and maximum cell wall thickness,cell size,and unit cell type on the mechanical performance of TPU FGCS structures.The results indicated that a Schwarz P FGCS structure with cell size,minimum and maximum cell wall thickness of 6,0.9 and 2.8 mm,respectively,could be optimal for a compromise between performance and weight.In this optimized case,stiffness and volume fraction values of 684 N/mm and 0.64 were obtained,respectively.The study also presents a proof-of-concept design for a BK prosthetic damper,highlighting the potential of FGCSs to enhance patient comfort,reduce manufacturing costs,and enable personalised designs through 3D scanning and AM.The obtained results could be a step forward towards the incorporation of AM technologies in prosthetics,offering a pathway to lightweight,cost-effective,and functionally tailored solutions.
基金financially supported by the Guangdong Major Project of Basic Research(No.2023B0303000002)Shenzhen Science and Technology Plan Project(No.SGDX20230116091644003)+3 种基金Shenzhen Key Laboratory of Advanced Energy Storage(No.ZDSYS20220401141000001)high-level special funds(No.G03034K001)the Guangxi Key Technologies R&D Program(AB23075171,AB25069180)National Natural Science Foundation of China(22265007,52263016)。
文摘With the escalating demand for safe,sustainable,and high-performance energy storage systems,hydrogel electrolytes have emerged as promising alternatives to conventional liquid electrolytes in zinc-ion batteries.By integrating the high ionic conductivity of liquid electrolytes with the mechanical robustness of solid frameworks,hydrogel electrolytes offer distinct advantages in suppressing zinc dendrite formation,enhancing interfacial stability,and enabling reliable operation under extreme environmental conditions.This review systematically summarizes the fundamental characteristics and design criteria of hydrogel electrolytes,including mechanical flexibility,ionic transport capabilities,and environmental adaptability.It further explores various compositional design strategies involving natural polymers,synthetic polymers,and composite systems,as well as the incorporation of electrolyte salts and functional additives.In addition,recent advances in functional optimization,such as anti-freezing properties,self-healing abilities,thermal responsiveness,and biocompatibility,are comprehensively discussed.Finally,the review outlines the current challenges and proposes potential directions for future research.
基金supported by the National Science Fund for Distinguished Young Scholars(62025602)the National Natural Science Foundation of China(62373302,U22B2036)+2 种基金the National Key Research and Development Program of China(2024YFF0509600)the Fundamental Research Funds for the Central Universities(G2024WD0151,D5000240309)the Tencent Foundation and XPLORER PRIZE。
文摘In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperative behavior of players in the system and introduce the concept of positive-incentive noise in CAID.We design a global cost function to ensure convergence of the system can be achieved and strive to improve the final level of cooperation.An optimal CAID control method is proposed to derive the deterministic optimal learning rate in analytical form,avoiding the variability and uncertainty brought about by neural network fitting or parameter adjustment.On this basis,the convergence of the dynamic model is further analyzed by using the Lyapunov function instead of the Jacobian matrix.Additionally,an adaptive filtering mechanism is designed to dynamically ensure that only positive-incentive noise affects the system,effectively reducing the impact of negative noise and enhancing system stability.The framework is validated through simulations involving triple classical game models,including the hawk-dove game,the stag hunt game,the chicken game on networks,and a straightforward illustrative example.
基金partially supported by the Center for Advanced Systems Understanding (CASUS), financed by Germany’s Federal Ministry of Education and Research and the Saxon State Government out of the State Budget approved by the Saxon State Parliamentthe European Union’s Just Transition Fund (JTF) within the project Röntgenlaser Optimierung der Laserfusion (ROLF), Contract No. 5086999001, co-financed by the Saxon State Government out of the State Budget approved by the Saxon State Parliament+3 种基金the European Research Council (ERC) under the European Union’s Horizon 2022 Research and Innovation Programme (Grant Agreement No. 101076233, “PREXTREME”)Computations were performed on a Bull Cluster at the Center for Information Services and High-Performance Computing (ZIH) at Technische Universität Dresden and at the Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen (HLRN) under Grant No. mvp00024support by the National Natural Science Foundation of China under Grant No. 12274171support by the Advanced Materials–National Science and Technology Major Project (Grant No. 2024ZD0606900)
文摘Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse of warm dense matter theory is thermal density functional theory(DFT),which,however,suffers from two limitations:(i)its accuracy can depend on the utilized exchange-correlation functional,which has to be approximated,and(ii)it is generally limited to single-electron properties such as the density distribution.Here,we present a new ansatz combining time-dependent DFT results for the dynamic structure factor S_(ee)(q,ω)with static DFT results for the density response.This allows us to estimate the electron-electron static structure factor S_(ee)(q)of warm dense hydrogen with high accuracy over a broad range of densities and temperatures.In addition to its value for the study of warm dense matter,our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.
文摘Multi</span><span><span style="font-family:"">-</span></span><span><span style="font-family:"">goal and multi-objective optimizations are similar techniques to</span></span><span><span style="font-family:""> achieve <span>multiple conflicting goals/objectives simultaneously. There are several tech</span>niques <span>for solving multi-goal and multi-objective optimization problems. The</span> <span>present </span><span>study proposed the possibility of convertibility in solving multi-goal and mul</span>ti-objective optimization problems.
文摘A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good performance. The computation results show that its generality, precision, robustness, simplicity and performance are all satisfactory.
基金the Research Fund for the Doctoral Program of Higher Education of China (20020008004).
文摘An adaptive immune-genetic algorithm （AIGA） is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response （secondary response）, adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.
基金Supported by National Natural Science Foundation of China (Grant Nos.51105040,11372036)Aeronautical Science Foundation of China (Grant Nos.2011ZA72003,2009ZA72002)+1 种基金Excellent Young Scholars Research Fund of Beijing Institute of Technology (Grant No.2010Y0102)Foundation Research Fund of Beijing Institute of Technology (Grant No.20130142008)
文摘As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function（SEO-SRBF）. Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of thematerial volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.
基金Supported by the National Natural Science Foundation of China (70071042,60073043,60133010)
文摘This paper presents a two-phase genetic algorithm (TPGA) based on the multi- parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population' s evol vement to diversity or convergence according to the population size and the crossover size, so we make it run in different forms during the global and local optimization phases and then forms TPGA. The experiment results show that TPGA is very efficient for the optimization of low-dimension multi-modal functions, usually we can obtain all the global optimal solutions.
文摘The filled function method is an approach for finding a global minimum of multi-dimensional functions. With more and more relevant research, it becomes a promising way used in unconstrained global optimization. Some filled functions with one or two parameters have already been suggested. However, there is no certain criterion to choose a parameter appropriately. In this paper, a parameter-free filled function was proposed. The definition of the original filled function and assumptions of the objective function given by Ge were improved according to the presented parameter-free filled function. The algorithm and numerical results of test functions were reported. Conclusions were drawn in the end. Key words global optimization - filled function method - local minimizer MSC 2000 90C30
文摘Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
文摘Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.
基金Supported by the National Natural Science Foundation of China(60133010,60073043,70071042)
文摘This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.
基金performed in the laboratuary of Istanbul University,Faculty of Forestry where the authors are employed at present
文摘In this study,2~5 （five factors at two-level factorial design） design of experiment was applied to investigate a set of optimal machining parameters to achieve a minimum surface roughness value for Abies nordmanniana.Wood specimens were prepared using different values of spindle speed,feed rate,depth of cut,tool radius,and cutting directions.Average surface roughness （R_z） values were applied using a stylus.The objectives were to：（1）obtain the effective variables of wood surface roughness;（2） analyze which of these factors had an impact on variability in the CNC machining process;（3） evaluate the optimal cutting values within the range of different cutting levels of machining parameters.The results indicate that the design of experiment（DOE） based on the desirability function approach determined the optimal machining parameters successfully,leading to minimum R_a compared to the observed value.Minimum surface roughness values of tangential and radial cutting directions were 3.58 and 3.21 μm,respectively.
