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
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
Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resour...Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.展开更多
The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increas...The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.展开更多
Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for ...Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.展开更多
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
Most material distribution-based topology optimization methods work on a relaxed form of the optimization problem and then push the solution toward the binary limits.However,when benchmarking these methods,researchers...Most material distribution-based topology optimization methods work on a relaxed form of the optimization problem and then push the solution toward the binary limits.However,when benchmarking these methods,researchers use known solutions to only a single form of benchmark problem.This paper proposes a comparison platform for systematic benchmarking of topology optimization methods using both binary and relaxed forms.A greyness measure is implemented to evaluate how far a solution is from the desired binary form.The well-known ZhouRozvany(ZR)problem is selected as the benchmarking problem here,making use of available global solutions for both its relaxed and binary forms.The recently developed non-penalization Smooth-edged Material Distribution for Optimizing Topology(SEMDOT),well-established Solid Isotropic Material with Penalization(SIMP),and continuation methods are studied on this platform.Interestingly,in most cases,the grayscale solutions obtained by SEMDOT demonstrate better performance in dealing with the ZR problem than SIMP.The reasons are investigated and attributed to the usage of two different regularization techniques,namely,the Heaviside smooth function in SEMDOT and the power-law penalty in SIMP.More importantly,a simple-to-use benchmarking graph is proposed for evaluating newly developed topology optimization methods.展开更多
Magnetic skyrmions are recognized as potential information carriers for building the next-generation spintronic memory and logic devices.Towards functional device applications,efficient electrical detection of skyrmio...Magnetic skyrmions are recognized as potential information carriers for building the next-generation spintronic memory and logic devices.Towards functional device applications,efficient electrical detection of skyrmions at room temperature is one of the most important prerequisites.展开更多
This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration s...This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.展开更多
With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both local...With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.展开更多
A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linea...A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.展开更多
With the continuous advancement in topology optimization and additive manufacturing(AM)technology,the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying micr...With the continuous advancement in topology optimization and additive manufacturing(AM)technology,the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly.However,a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures,potentially resulting in diminished efficiency or macroscopic failure.A Hybrid Level Set Method(HLSM)is proposed,specifically designed to enhance connectivity among non-uniform microstructures,contributing to the design of functionally graded cellular structures.The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces.Initially,an interpolation algorithm is presented to construct transition microstructures seamlessly connected on both sides.Subsequently,the algorithm enables the morphing of non-uniform unit cells to seamlessly adapt to interconnected adjacent microstructures.The method,seamlessly integrated into a multi-scale topology optimization framework using the level set method,exhibits its efficacy through numerical examples,showcasing its prowess in optimizing 2D and 3D functionally graded materials(FGM)and multi-scale topology optimization.In essence,the pressing issue of interface connections in complex structure design is not only addressed but also a robust methodology is introduced,substantiated by numerical evidence,advancing optimization capabilities in the realm of functionally graded materials and cellular structures.展开更多
In the evolving landscape of artificial intelligence and machine learning, the choice of optimization algorithm can significantly impact the success of model training and the accuracy of predictions. This paper embark...In the evolving landscape of artificial intelligence and machine learning, the choice of optimization algorithm can significantly impact the success of model training and the accuracy of predictions. This paper embarks on a rigorous and comprehensive exploration of widely adopted optimization techniques, specifically focusing on their performance when applied to the notoriously challenging Rosenbrock function. As a benchmark problem known for its deceptive curvature and narrow valleys, the Rosenbrock function provides a fertile ground for examining the nuances and intricacies of algorithmic behavior. The study delves into a diverse array of optimization methods, including traditional Gradient Descent, its stochastic variant (SGD), and the more sophisticated Gradient Descent with Momentum. The investigation further extends to adaptive methods like RMSprop, AdaGrad, and the highly regarded Adam optimizer. By meticulously analyzing and visualizing the optimization paths, convergence rates, and gradient norms, this paper uncovers critical insights into the strengths and limitations of each technique. Our findings not only illuminate the intricate dynamics of these algorithms but also offer actionable guidance for their deployment in complex, real-world optimization problems. This comparative analysis promises to intrigue and inspire researchers and practitioners alike, as it reveals the subtle yet profound impacts of algorithmic choices in the quest for optimization excellence.展开更多
This research paper investigates the interface design and functional optimization of Chinese learning apps through the lens of user experience.With the increasing popularity of Chinese language learning apps in the er...This research paper investigates the interface design and functional optimization of Chinese learning apps through the lens of user experience.With the increasing popularity of Chinese language learning apps in the era of rapid mobile internet development,users'demands for enhanced interface design and interaction experience have grown significantly.The study aims to explore the influence of user feedback on the design and functionality of Chinese learning apps,proposing optimization strategies to improve user experience and learning outcomes.By conducting a comprehensive literature review,utilizing methods such as surveys and user interviews for data collection,and analyzing user feedback,this research identifies existing issues in the interface design and interaction experience of Chinese learning apps.The results present user opinions,feedback analysis,identified problems,improvement directions,and specific optimization strategies.The study discusses the potential impact of these optimization strategies on enhancing user experience and learning outcomes,compares findings with previous research,addresses limitations,and suggests future research directions.In conclusion,this research contributes to enriching the design theory of Chinese learning apps,offering practical optimization recommendations for developers,and supporting the continuous advancement of Chinese language learning apps.展开更多
This paper presents a numerical investigation of the potential aerodynamic benefits of using endwall contouring in a fairly aggressive duct with six struts based on the platform for endwall design optimization.The pla...This paper presents a numerical investigation of the potential aerodynamic benefits of using endwall contouring in a fairly aggressive duct with six struts based on the platform for endwall design optimization.The platform is constructed by integrating adaptive genetic algorithm（AGA）, design of experiments（DOE）, response surface methodology（RSM） based on the artificial neural network（ANN）, and a 3D Navier–Stokes solver.The visual analysis method based on DOE is used to define the design space and analyze the impact of the design parameters on the target function（response）.Optimization of the axisymmetric and the non-axisymmetric endwall contouring in an S-shaped duct is performed and evaluated to minimize the total pressure loss.The optimal ducts are found to reduce the hub corner separation and suppress the migration of the low momentum fluid.The non-axisymmetric endwall contouring is shown to remove the separation completely and reduce the net duct loss by 32.7%.展开更多
This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optima...This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.展开更多
In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve...In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.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
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 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funded by the KRICT Project (KK2512-10) of the Korea Research Institute of Chemical Technology and the Ministry of Trade, Industry and Energy (MOTIE)the Korea Institute for Advancement of Technology (KIAT) through the Virtual Engineering Platform Program (P0022334)+1 种基金supported by the Carbon Neutral Industrial Strategic Technology Development Program (RS-202300261088) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)Further support was provided by research fund of Chungnam National University。
文摘Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.
基金Funded by the Deep Underground National Science&Technology Major Project gram of China(No.2024ZD1003704)the National Natural Science Foundation of China(Nos.51834001 and 52374111)。
文摘The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.
基金supported in part by the National Natural Science Foundation of China(62276119)the Natural Science Foundation of Jiangsu Province(BK20241764)the Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX22_2860)
文摘Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
文摘Most material distribution-based topology optimization methods work on a relaxed form of the optimization problem and then push the solution toward the binary limits.However,when benchmarking these methods,researchers use known solutions to only a single form of benchmark problem.This paper proposes a comparison platform for systematic benchmarking of topology optimization methods using both binary and relaxed forms.A greyness measure is implemented to evaluate how far a solution is from the desired binary form.The well-known ZhouRozvany(ZR)problem is selected as the benchmarking problem here,making use of available global solutions for both its relaxed and binary forms.The recently developed non-penalization Smooth-edged Material Distribution for Optimizing Topology(SEMDOT),well-established Solid Isotropic Material with Penalization(SIMP),and continuation methods are studied on this platform.Interestingly,in most cases,the grayscale solutions obtained by SEMDOT demonstrate better performance in dealing with the ZR problem than SIMP.The reasons are investigated and attributed to the usage of two different regularization techniques,namely,the Heaviside smooth function in SEMDOT and the power-law penalty in SIMP.More importantly,a simple-to-use benchmarking graph is proposed for evaluating newly developed topology optimization methods.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1405100)the NSFC distinguished Young Scholar program(Grant No.12225409)+6 种基金the Basic Science Center Project of National Natural Science Foundation of China(NSFC)(Grant No.52388201)the NSFC general program(Grant Nos.52271181,51831005,and 12421004)the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300500)Beijing Natural Science Foundation(Grant No.Z240006)supported by the KAUST Office of Sponsored Research(OSR)under Award Nos.ORA-CRG102021-4665 and ORA-CRG11-2022-5031supported by the National Key Research and Development Program of China(No.2024YFA1408503)Sichuan Province Science and Technology Support Program(No.2025YFHZ0147)。
文摘Magnetic skyrmions are recognized as potential information carriers for building the next-generation spintronic memory and logic devices.Towards functional device applications,efficient electrical detection of skyrmions at room temperature is one of the most important prerequisites.
基金funded by the National Natural Science Foundation of China(12102487)Basic and Applied Basic Research Foundation of Guangdong Province,China(2023A1515012339)+1 种基金Shenzhen Science and Technology Program(ZDSYS20210623091808026)the Discovery Grant(RGPIN-2024-06290)of the Natural Sciences and Engineering Research Council of Canada。
文摘This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.
基金the Fundamental Research Program of Guangdong,China,under Grants 2020B1515310023 and 2023A1515011281in part by the National Natural Science Foundation of China under Grant 61571005.
文摘With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.
基金Project supported by the National Natural Science Foundation of China (Nos.12072007,12072006,12132001,and 52192632)the Ningbo Natural Science Foundation of Zhejiang Province of China (No.202003N4018)the Defense Industrial Technology Development Program of China (Nos.JCKY2019205A006,JCKY2019203A003,and JCKY2021204A002)。
文摘A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.
基金the National Key Research and Development Program of China(Grant Number 2021YFB1714600)the National Natural Science Foundation of China(Grant Number 52075195)the Fundamental Research Funds for the Central Universities,China through Program No.2172019kfyXJJS078.
文摘With the continuous advancement in topology optimization and additive manufacturing(AM)technology,the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly.However,a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures,potentially resulting in diminished efficiency or macroscopic failure.A Hybrid Level Set Method(HLSM)is proposed,specifically designed to enhance connectivity among non-uniform microstructures,contributing to the design of functionally graded cellular structures.The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces.Initially,an interpolation algorithm is presented to construct transition microstructures seamlessly connected on both sides.Subsequently,the algorithm enables the morphing of non-uniform unit cells to seamlessly adapt to interconnected adjacent microstructures.The method,seamlessly integrated into a multi-scale topology optimization framework using the level set method,exhibits its efficacy through numerical examples,showcasing its prowess in optimizing 2D and 3D functionally graded materials(FGM)and multi-scale topology optimization.In essence,the pressing issue of interface connections in complex structure design is not only addressed but also a robust methodology is introduced,substantiated by numerical evidence,advancing optimization capabilities in the realm of functionally graded materials and cellular structures.
文摘In the evolving landscape of artificial intelligence and machine learning, the choice of optimization algorithm can significantly impact the success of model training and the accuracy of predictions. This paper embarks on a rigorous and comprehensive exploration of widely adopted optimization techniques, specifically focusing on their performance when applied to the notoriously challenging Rosenbrock function. As a benchmark problem known for its deceptive curvature and narrow valleys, the Rosenbrock function provides a fertile ground for examining the nuances and intricacies of algorithmic behavior. The study delves into a diverse array of optimization methods, including traditional Gradient Descent, its stochastic variant (SGD), and the more sophisticated Gradient Descent with Momentum. The investigation further extends to adaptive methods like RMSprop, AdaGrad, and the highly regarded Adam optimizer. By meticulously analyzing and visualizing the optimization paths, convergence rates, and gradient norms, this paper uncovers critical insights into the strengths and limitations of each technique. Our findings not only illuminate the intricate dynamics of these algorithms but also offer actionable guidance for their deployment in complex, real-world optimization problems. This comparative analysis promises to intrigue and inspire researchers and practitioners alike, as it reveals the subtle yet profound impacts of algorithmic choices in the quest for optimization excellence.
文摘This research paper investigates the interface design and functional optimization of Chinese learning apps through the lens of user experience.With the increasing popularity of Chinese language learning apps in the era of rapid mobile internet development,users'demands for enhanced interface design and interaction experience have grown significantly.The study aims to explore the influence of user feedback on the design and functionality of Chinese learning apps,proposing optimization strategies to improve user experience and learning outcomes.By conducting a comprehensive literature review,utilizing methods such as surveys and user interviews for data collection,and analyzing user feedback,this research identifies existing issues in the interface design and interaction experience of Chinese learning apps.The results present user opinions,feedback analysis,identified problems,improvement directions,and specific optimization strategies.The study discusses the potential impact of these optimization strategies on enhancing user experience and learning outcomes,compares findings with previous research,addresses limitations,and suggests future research directions.In conclusion,this research contributes to enriching the design theory of Chinese learning apps,offering practical optimization recommendations for developers,and supporting the continuous advancement of Chinese language learning apps.
基金supported by the National Natural Science Foundation of China (Nos.51006005, 51236001)the National Basic Research Program of China (No.2012CB720201)the Fundamen tal Research Funds for the Central Universities of China
文摘This paper presents a numerical investigation of the potential aerodynamic benefits of using endwall contouring in a fairly aggressive duct with six struts based on the platform for endwall design optimization.The platform is constructed by integrating adaptive genetic algorithm（AGA）, design of experiments（DOE）, response surface methodology（RSM） based on the artificial neural network（ANN）, and a 3D Navier–Stokes solver.The visual analysis method based on DOE is used to define the design space and analyze the impact of the design parameters on the target function（response）.Optimization of the axisymmetric and the non-axisymmetric endwall contouring in an S-shaped duct is performed and evaluated to minimize the total pressure loss.The optimal ducts are found to reduce the hub corner separation and suppress the migration of the low momentum fluid.The non-axisymmetric endwall contouring is shown to remove the separation completely and reduce the net duct loss by 32.7%.
基金King Saud University for funding this research through Researchers Supporting Program Number(RSPD2023R704),King Saud University,Riyadh,Saudi Arabia.
文摘This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
基金supported in part by National Natural Science Foundation of China(62106230,U23A20340,62376253,62176238)China Postdoctoral Science Foundation(2023M743185)Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications Open Fundation(BDIC-2023-A-007)。
文摘In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.
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.
