To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engi...To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.展开更多
Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become...Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become a significant instrument for propelling the energy revolution and ensuring energy security.Under increasingly stringent carbon emission constraints,how to achieve multi-dimensional improvements in energy utilization efficiency,renewable energy accommodation levels,and system economics-through the intelligent coupling of diverse energy carriers such as electricity,heat,natural gas,and hydrogen,and the effective application of market-based instruments like carbon trading and demand response-constitutes a critical scientific and engineering challenge demanding urgent solutions.展开更多
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke...The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.展开更多
Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they prop...Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex geometries.This method efficiently expands the de...Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex geometries.This method efficiently expands the design space,allowing for the creation of products with better performance than ever before.With the emergence of new manufacturing technologies,new design methods are required to efficiently utilize the expanded design space.Therefore,topology optimization methods have attracted the attention of researchers because of their ability to generate new and optimized designs without requiring prior experience.The combination of AM and topology optimization has proven to be a powerful tool for structural innovation in design and manufacturing.However,it is important to note that AM does not eliminate all manufacturing restrictions but instead replaces them with a different set of design considerations that designers must consider for the successful implementation of these technologies.This has motivated research on topology optimization methods that incorporate manufacturable constraints for AM structures.In this paper,we present a survey of the latest studies in this research area,with a particular focus on developments in China.Additionally,we discuss the existing research gaps and future development trends.展开更多
The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimizati...The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimization problem.Currently,a comprehensive framework for the structural design and optimization of compound balanced BPUs is lacking.Therefore,this study proposes a novel structural design scheme for BPUs,aiming to meet the practical needs of designers and operators by sequentially optimizing both the dynamic characteristics and balance properties of the BPUs.A dynamic model of compound balanced BPU was established based on D'Alembert's principle.The constraints for structural dimensions were formulated based on the actual operational requirements and design experience with BPUs.To optimize the structure,three algorithms were employed:the particle swarm optimization(PSO)algorithm,the genetic algorithm(GA),and the gray wolf optimization(GWO)algorithm.Each newly generated individuals are regulated by constraints to ensure the rationality of the outcomes.Furthermore,the integration of three algorithms ensures the increased likelihood of attaining the global optimal solution.The polished rod acceleration of the optimized structure is significantly reduced,and the dynamic characteristics of the up and down strokes are essentially symmetrical.Additionally,these three algorithms are also applied to the balance optimization of BPUs based on the measured dynamometer card.The calculation results demonstrate that the GWO-based optimization method exhibits excellent robustness in terms of structural optimization by enhancing the operational smoothness of the BPU,as well as in balance optimization by achieving energy conservation.By applying the optimization scheme proposed in this paper,the CYJW7-3-23HF type of BPU was designed,achieving a maximum polished rod acceleration of±0.675 m/s^(2) when operating at a stroke of 6 min^(−1).When deployed in two wells,the root-mean-square(RMS)torque was minimized,reaching values of 7.539 kN·m and 12.921 kN·m,respectively.The proposed design method not only contributes to the personalized customization but also improves the design efficiency of compound balanced BPUs.展开更多
Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the dela...Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.展开更多
This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process.Building upon established multiscale methodologies...This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process.Building upon established multiscale methodologies,we develop a new framework incorporating yield stress limits either as constraints or objectives alongside previously established local and global buckling constraints.This approach significantly refines the optimization process,ensuring that the resulting designs meet mechanical performance criteria and adhere to critical material yield constraints.First,we establish local density-dependent von Mises yield surfaces based on local yield estimates from homogenization-based analysis to predict the local yield limits of the homogenized materials.Then,these local yield-based load factors are combined with local and global buckling criteria to obtain topology optimized designs that consider yield and buckling failure on all levels.This integration is crucial for the practical application of optimized structures in real-world scenarios,where material yield and stability behavior critically influence structural integrity and durability.Numerical examples demonstrate how optimized designs depend on the stiffness to yield ratio of the considered building material.Despite the foundational assumption of the separation of scales,the de-homogenized structures,even at relatively coarse length scales,exhibit a remarkably high degree of agreement with the corresponding homogenized predictions.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
In this paper,a topology optimization method for coordinated stiffness and strength design is proposed under mass constraints,utilizing the Solid Isotropic Material with Penalization approach.Element densities are reg...In this paper,a topology optimization method for coordinated stiffness and strength design is proposed under mass constraints,utilizing the Solid Isotropic Material with Penalization approach.Element densities are regulated through sensitivity filtering tomitigate numerical instabilities associatedwith stress concentrations.Ap-norm aggregation function is employed to globalize local stress constraints,and a normalization technique linearly weights strain energy and stress,transforming the multi-objective problem into a single-objective formulation.The sensitivity of the objective function with respect to design variables is rigorously derived.Three numerical examples are presented,comparing the optimized structures in terms of strain energy,mass,and stress across five different mathematical models with varying combinations of optimization objectives.The results validate the effectiveness and feasibility of the proposed method for achieving a balanced design between structural stiffness and strength.This approach offers a new perspective for future research on stiffness-strength coordinated structural optimization.展开更多
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr...This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.展开更多
This paper presents an improved level set method for topology optimization of geometrically nonlinear structures accounting for the effect of thermo-mechanical couplings.It derives a new expression for element couplin...This paper presents an improved level set method for topology optimization of geometrically nonlinear structures accounting for the effect of thermo-mechanical couplings.It derives a new expression for element coupling stress resulting from the combination of mechanical and thermal loading,using geometric nonlinear finite element analysis.A topological model is then developed to minimize compliance while meeting displacement and frequency constraints to fulfill design requirements of structural members.Since the conventional Lagrange multiplier search method is unable to handle convergence instability arising from large deformation,a novel Lagrange multiplier search method is proposed.Additionally,the proposed method can be extended to multi-constrained geometrically nonlinear topology optimization,accommodating multiple physical field couplings.展开更多
To evaluate the heat performance of the lifting-body entry vehicle during the hypersonic gliding phase,entry flight heat tests involving the determination of the maximum peak-heat-flux entry trajectory with complex co...To evaluate the heat performance of the lifting-body entry vehicle during the hypersonic gliding phase,entry flight heat tests involving the determination of the maximum peak-heat-flux entry trajectory with complex constraints are essential.A significant obstacle is the uncertainty of passage time or energy states of the maximum peak entry heat flux point and waypoints.This paper showcases an endeavour to leverage disjunctive programming and combinatorial theory for the max-max type(maximum peak-heat-flux)Entry Trajectory Optimization(ETO)problems with complex constraints such as dynamic pressure,normal load,waypoints,and no-fly zones.The concept of a"generalized waypoint"is introduced,and the maximum peak-heat-flux point is regarded as a"generalized waypoint".Through the application of propositional calculus rules,the derivation of generalized waypoints incorporating various physical quantities and magnitudes such as heat flux density,longitude,and latitude is actualized in one disjunctive normal form,enabling resolution via a unified method.Consequently,a novel method based on combinatorial prior rules is proposed,utilizing Successive Mixed-Integer Nonlinear Programming(SMINLP)to optimize various heat entry test flight trajectories.Numerical experiments are provided to show the computational accuracy,stability,and adaptability of the proposed method in solving maxmax type entry optimal control problems.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of m...This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design.The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads.The topology optimization formula is combined with the ordered solid isotropic material with penalization(ordered-SIMP)multi-material interpolation model.The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function.Furthermore,the sequential optimization and reliability assessment(SORA)is applied to transform the original uncertainty optimization problem into an equivalent deterministic topology optimization(DTO)problem.Stochastic response surface and sparse grid technique are combined with SORA to get accurate information on the most probable failure point(MPP).In each cycle,the equivalent topology optimization formula is updated according to the MPP information obtained in the previous cycle.The adjoint variable method is used for deriving the sensitivity of the stress constraint and the moving asymptote method(MMA)is used to update design variables.Finally,the validity and feasibility of the method are verified by the numerical example of L-shape beam design,T-shape structure design,steering knuckle,and 3D T-shaped beam.展开更多
The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on...The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization problems.In this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual vari-ables.By satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence rate.This only requires assuming convexity of the objective function.We validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.展开更多
Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In ...Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.展开更多
In density-based topological design, one expects that the final result consists of elements either black （solid material） or white （void）, without any grey areas. Moreover, one also expects that the optimal topolo...In density-based topological design, one expects that the final result consists of elements either black （solid material） or white （void）, without any grey areas. Moreover, one also expects that the optimal topology can be obtained by starting from any initial topology configuration. An improved structural topological optimization method for multidisplacement constraints is proposed in this paper. In the proposed method, the whole optimization process is divided into two optimization adjustment phases and a phase transferring step. Firstly, an optimization model is built to deal with the varied displacement limits, design space adjustments, and reasonable relations between the element stiffness matrix and mass and its element topology variable. Secondly, a procedure is proposed to solve the optimization problem formulated in the first optimization adjustment phase, by starting with a small design space and advancing to a larger deign space. The design space adjustments are automatic when the design domain needs expansions, in which the convergence of the proposed method will not be affected. The final topology obtained by the proposed procedure in the first optimization phase, can approach to the vicinity of the optimum topology. Then, a heuristic algorithm is given to improve the efficiency and make the designed structural topology black/white in both the phase transferring step and the second optimization adjustment phase. And the optimum topology can finally be obtained by the second phase optimization adjustments. Two examples are presented to show that the topologies obtained by the proposed method are of very good 0/1 design distribution property, and the computational efficiency is enhanced by reducing the element number of the design structural finite model during two optimization adjustment phases. And the examples also show that this method is robust and practicable.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFB3706704)the Academician Special Science Research Project of CCCC(No.YSZX-03-2022-01-B).
文摘To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.
文摘Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become a significant instrument for propelling the energy revolution and ensuring energy security.Under increasingly stringent carbon emission constraints,how to achieve multi-dimensional improvements in energy utilization efficiency,renewable energy accommodation levels,and system economics-through the intelligent coupling of diverse energy carriers such as electricity,heat,natural gas,and hydrogen,and the effective application of market-based instruments like carbon trading and demand response-constitutes a critical scientific and engineering challenge demanding urgent solutions.
基金supported by the Natural Science Foundation of Anhui Province(Grant Number 2208085MG181)the Science Research Project of Higher Education Institutions in Anhui Province,Philosophy and Social Sciences(Grant Number 2023AH051063)the Open Fund of Key Laboratory of Anhui Higher Education Institutes(Grant Number CS2021-ZD01).
文摘The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.
基金supported in part by the National Key Research and Development Program of China(2022YFD2001200)the National Natural Science Foundation of China(62176238,61976237,62206251,62106230)+3 种基金China Postdoctoral Science Foundation(2021T140616,2021M692920)the Natural Science Foundation of Henan Province(222300420088)the Program for Science&Technology Innovation Talents in Universities of Henan Province(23HASTIT023)the Program for Science&Technology Innovation Teams in Universities of Henan Province(23IRTSTHN010).
文摘Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金supported by National Natural Science Foundation of China(Grant Nos.12272076,U2341232,11332004,and U1808215)the 111 Project of China(Grant No.B14013).
文摘Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex geometries.This method efficiently expands the design space,allowing for the creation of products with better performance than ever before.With the emergence of new manufacturing technologies,new design methods are required to efficiently utilize the expanded design space.Therefore,topology optimization methods have attracted the attention of researchers because of their ability to generate new and optimized designs without requiring prior experience.The combination of AM and topology optimization has proven to be a powerful tool for structural innovation in design and manufacturing.However,it is important to note that AM does not eliminate all manufacturing restrictions but instead replaces them with a different set of design considerations that designers must consider for the successful implementation of these technologies.This has motivated research on topology optimization methods that incorporate manufacturable constraints for AM structures.In this paper,we present a survey of the latest studies in this research area,with a particular focus on developments in China.Additionally,we discuss the existing research gaps and future development trends.
基金supported by the Key Laboratory of Petroleum and Natural Gas Equipment,Ministry of Education(No.OGE202303-08)Engineering Technology Research Center for Industrial Internet of Things and Intelligent Sensing,Hubei Province(No.KXZ 202203).
文摘The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimization problem.Currently,a comprehensive framework for the structural design and optimization of compound balanced BPUs is lacking.Therefore,this study proposes a novel structural design scheme for BPUs,aiming to meet the practical needs of designers and operators by sequentially optimizing both the dynamic characteristics and balance properties of the BPUs.A dynamic model of compound balanced BPU was established based on D'Alembert's principle.The constraints for structural dimensions were formulated based on the actual operational requirements and design experience with BPUs.To optimize the structure,three algorithms were employed:the particle swarm optimization(PSO)algorithm,the genetic algorithm(GA),and the gray wolf optimization(GWO)algorithm.Each newly generated individuals are regulated by constraints to ensure the rationality of the outcomes.Furthermore,the integration of three algorithms ensures the increased likelihood of attaining the global optimal solution.The polished rod acceleration of the optimized structure is significantly reduced,and the dynamic characteristics of the up and down strokes are essentially symmetrical.Additionally,these three algorithms are also applied to the balance optimization of BPUs based on the measured dynamometer card.The calculation results demonstrate that the GWO-based optimization method exhibits excellent robustness in terms of structural optimization by enhancing the operational smoothness of the BPU,as well as in balance optimization by achieving energy conservation.By applying the optimization scheme proposed in this paper,the CYJW7-3-23HF type of BPU was designed,achieving a maximum polished rod acceleration of±0.675 m/s^(2) when operating at a stroke of 6 min^(−1).When deployed in two wells,the root-mean-square(RMS)torque was minimized,reaching values of 7.539 kN·m and 12.921 kN·m,respectively.The proposed design method not only contributes to the personalized customization but also improves the design efficiency of compound balanced BPUs.
基金supported by National Key Research and Development Program of China(2022YFB3305900)National Natural Science Foundation of China(62394343,62394345)+1 种基金Major Science and Technology Projects of Longmen Laboratory(NO.LMZDXM202206)Shanghai Rising-Star Program under Grant 24QA2706100.
文摘Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.
基金supported by Villum Fonden through the Villum Investigator Project“AMSTRAD”(Grant No.VIL54487).
文摘This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process.Building upon established multiscale methodologies,we develop a new framework incorporating yield stress limits either as constraints or objectives alongside previously established local and global buckling constraints.This approach significantly refines the optimization process,ensuring that the resulting designs meet mechanical performance criteria and adhere to critical material yield constraints.First,we establish local density-dependent von Mises yield surfaces based on local yield estimates from homogenization-based analysis to predict the local yield limits of the homogenized materials.Then,these local yield-based load factors are combined with local and global buckling criteria to obtain topology optimized designs that consider yield and buckling failure on all levels.This integration is crucial for the practical application of optimized structures in real-world scenarios,where material yield and stability behavior critically influence structural integrity and durability.Numerical examples demonstrate how optimized designs depend on the stiffness to yield ratio of the considered building material.Despite the foundational assumption of the separation of scales,the de-homogenized structures,even at relatively coarse length scales,exhibit a remarkably high degree of agreement with the corresponding homogenized predictions.
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funded by National Nature Science Foundation of China(92266203)National Nature Science Foundation of China(52205278)+1 种基金Key Projects of Shijiazhuang Basic Research Program(241791077A)Central Guide Local Science and Technology Development Fund Project of Hebei Province(246Z1022G).
文摘In this paper,a topology optimization method for coordinated stiffness and strength design is proposed under mass constraints,utilizing the Solid Isotropic Material with Penalization approach.Element densities are regulated through sensitivity filtering tomitigate numerical instabilities associatedwith stress concentrations.Ap-norm aggregation function is employed to globalize local stress constraints,and a normalization technique linearly weights strain energy and stress,transforming the multi-objective problem into a single-objective formulation.The sensitivity of the objective function with respect to design variables is rigorously derived.Three numerical examples are presented,comparing the optimized structures in terms of strain energy,mass,and stress across five different mathematical models with varying combinations of optimization objectives.The results validate the effectiveness and feasibility of the proposed method for achieving a balanced design between structural stiffness and strength.This approach offers a new perspective for future research on stiffness-strength coordinated structural optimization.
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202401501,KJZD-M202401501).
文摘This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.
基金supported by grants from the National Natural Science Foundation of China (51478130)the Guangzhou Municipal Education Bureau’s Scientific Research Project, China (2024312217)+1 种基金the China Scholarship Council (201808440070)the 111 Project of China (D21021).
文摘This paper presents an improved level set method for topology optimization of geometrically nonlinear structures accounting for the effect of thermo-mechanical couplings.It derives a new expression for element coupling stress resulting from the combination of mechanical and thermal loading,using geometric nonlinear finite element analysis.A topological model is then developed to minimize compliance while meeting displacement and frequency constraints to fulfill design requirements of structural members.Since the conventional Lagrange multiplier search method is unable to handle convergence instability arising from large deformation,a novel Lagrange multiplier search method is proposed.Additionally,the proposed method can be extended to multi-constrained geometrically nonlinear topology optimization,accommodating multiple physical field couplings.
基金funded by the Key Laboratory of Cross-Domain Flight Interdisciplinary Technology,China(No.2024-KF02201)the National Natural Science Foundation of China(No.61973326)。
文摘To evaluate the heat performance of the lifting-body entry vehicle during the hypersonic gliding phase,entry flight heat tests involving the determination of the maximum peak-heat-flux entry trajectory with complex constraints are essential.A significant obstacle is the uncertainty of passage time or energy states of the maximum peak entry heat flux point and waypoints.This paper showcases an endeavour to leverage disjunctive programming and combinatorial theory for the max-max type(maximum peak-heat-flux)Entry Trajectory Optimization(ETO)problems with complex constraints such as dynamic pressure,normal load,waypoints,and no-fly zones.The concept of a"generalized waypoint"is introduced,and the maximum peak-heat-flux point is regarded as a"generalized waypoint".Through the application of propositional calculus rules,the derivation of generalized waypoints incorporating various physical quantities and magnitudes such as heat flux density,longitude,and latitude is actualized in one disjunctive normal form,enabling resolution via a unified method.Consequently,a novel method based on combinatorial prior rules is proposed,utilizing Successive Mixed-Integer Nonlinear Programming(SMINLP)to optimize various heat entry test flight trajectories.Numerical experiments are provided to show the computational accuracy,stability,and adaptability of the proposed method in solving maxmax type entry optimal control problems.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by the National Natural Science Foundation of China(Grant 52175236).
文摘This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design.The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads.The topology optimization formula is combined with the ordered solid isotropic material with penalization(ordered-SIMP)multi-material interpolation model.The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function.Furthermore,the sequential optimization and reliability assessment(SORA)is applied to transform the original uncertainty optimization problem into an equivalent deterministic topology optimization(DTO)problem.Stochastic response surface and sparse grid technique are combined with SORA to get accurate information on the most probable failure point(MPP).In each cycle,the equivalent topology optimization formula is updated according to the MPP information obtained in the previous cycle.The adjoint variable method is used for deriving the sensitivity of the stress constraint and the moving asymptote method(MMA)is used to update design variables.Finally,the validity and feasibility of the method are verified by the numerical example of L-shape beam design,T-shape structure design,steering knuckle,and 3D T-shaped beam.
基金supported by the National Natural Science Foundation of China(62176218,62176027)the Fundamental Research Funds for the Central Universities(XDJK2020TY003)the Funds for Chongqing Talent Plan(cstc2024ycjh-bgzxm0082)。
文摘The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization problems.In this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual vari-ables.By satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence rate.This only requires assuming convexity of the objective function.We validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.
基金Supported by National Natural Science Foundation of China(Grant Nos.51275366,50875190,51305311)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20134219110002)
文摘Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
基金supported by the National Natural Science Foundation of China (10872036)the High Technological Research and Development Program of China (2008AA04Z118)the Airspace Natural Science Foundation (2007ZA23007)
文摘In density-based topological design, one expects that the final result consists of elements either black （solid material） or white （void）, without any grey areas. Moreover, one also expects that the optimal topology can be obtained by starting from any initial topology configuration. An improved structural topological optimization method for multidisplacement constraints is proposed in this paper. In the proposed method, the whole optimization process is divided into two optimization adjustment phases and a phase transferring step. Firstly, an optimization model is built to deal with the varied displacement limits, design space adjustments, and reasonable relations between the element stiffness matrix and mass and its element topology variable. Secondly, a procedure is proposed to solve the optimization problem formulated in the first optimization adjustment phase, by starting with a small design space and advancing to a larger deign space. The design space adjustments are automatic when the design domain needs expansions, in which the convergence of the proposed method will not be affected. The final topology obtained by the proposed procedure in the first optimization phase, can approach to the vicinity of the optimum topology. Then, a heuristic algorithm is given to improve the efficiency and make the designed structural topology black/white in both the phase transferring step and the second optimization adjustment phase. And the optimum topology can finally be obtained by the second phase optimization adjustments. Two examples are presented to show that the topologies obtained by the proposed method are of very good 0/1 design distribution property, and the computational efficiency is enhanced by reducing the element number of the design structural finite model during two optimization adjustment phases. And the examples also show that this method is robust and practicable.
