Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is...Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.展开更多
For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnet...For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.展开更多
Scanning ion conductance microscopy（SICM） is an emerging non-destructive surface topography characterization apparatus with nanoscale resolution. However, the low regulating frequency of probe in most existing modul...Scanning ion conductance microscopy（SICM） is an emerging non-destructive surface topography characterization apparatus with nanoscale resolution. However, the low regulating frequency of probe in most existing modulated current based SICM systems increases the system noise, and has difficulty in imaging sample surface with steep height changes. In order to enable SICM to have the capability of imaging surfaces with steep height changes, a novel probe that can be used in the modulated current based bopping mode is designed. The design relies on two piezoelectric ceramics with different travels to separate position adjustment and probe frequency regulation in the Z direction. To fiarther improve the resonant frequency of the probe, the material and the key dimensions for each component of the probe are optimized based on the multi-objective optimization method and the finite element analysis. The optimal design has a resonant frequency of above 10 kHz. To validate the rationality of the designed probe, microstructured grating samples are imaged using the homebuilt modulated current based SICM system. The experimental results indicate that the designed high frequency probe can effectively reduce the spike noise by 26% in the average number of spike noise. The proposed design provides a feasible solution for improving the imaging quality of the existing SICM systems which normally use ordinary probes with relatively low regulating frequency.展开更多
In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Op...Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.展开更多
Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-i...Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.展开更多
A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably m...A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.展开更多
For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search ...For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.展开更多
Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an a...Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many man- ufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of mul- tiple objective functions related to process performance and cost is necessary. In this work, a multi- objective optimal experimental design framework is proposed to enhance the ef ciency of online model-identi cation platforms. The proposed framework permits exibility in the choice of trade-off experimental design solutions, which are calculated online that is, during the execution of experiments. The application of this framework to improve the online identi cation of kinetic models in ow reactors is illustrated using a case study in which a kinetic model is identi ed for the esteri cation of benzoic acid and ethanol in a microreactor.展开更多
It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specificat...It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design. @ 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi：10.1063/2.1306306]展开更多
This paper proposes a new type of nonlinear controllers and a large phase angle allowance design method based on the multi-objective optimal control system. With the proposed method, the performance of the system beco...This paper proposes a new type of nonlinear controllers and a large phase angle allowance design method based on the multi-objective optimal control system. With the proposed method, the performance of the system becomes better than that of the original system. Then, an example of the radar servo system is designed with a large phase angle allowance multi-objective optimal design method. Finally, the performance based on computer simulation demonstrates that the multi-objective optimal system is superior to linear optimal systems.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
The work takes a new liquid-cooling plate in a power battery with pin fins inside the channel as the object.A mathematical model is established via the central composite design of the response surface to study the rel...The work takes a new liquid-cooling plate in a power battery with pin fins inside the channel as the object.A mathematical model is established via the central composite design of the response surface to study the relationships among the length,width,height,and spacing of pin fins;the maximum temperature and temperature difference of the battery module;and the pressure drop of the liquid-cooling plate.Model accuracy is verified via variance analysis.The new liquid-cooling plate enables the power battery to work within an optimal temperature range.Appropriately increasing the length,width,and height and reducing the spacing of pin fins could reduce the temperature of the power battery module and improve the temperature uniformity.However,the pressure drop of the liquid-cooling plate increases.The structural parameters of the pin fins are optimized to minimize the maximum temperature and the temperature difference of the battery module as well as the pressure drop of the liquid-cooling plate.The errors between the values predicted and actual by the simulation test are 0.58%,4%,and 0.48%,respectively,which further verifies the model accuracy.The results reveal the influence of the structural parameters of the pin fins inside the liquid-cooling plate on its heat dissipation performance and pressure drop characteristics.A theoretical basis is provided for the design of liquid-cooling plates in power batteries and the optimization of structural parameters.展开更多
Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the aut...Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene（PP） under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment（DOE）, response surface model（RSM）, and collaborative optimization（CO） are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm（MOGA）, the optimal Pareto sets are completed to solve the multi-objective optimization（MOO） problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.展开更多
Designing refractory high-entropy alloys(RHEAs)for high-temperature(HT)applications is an outstanding challenge given the vast possible composition space,which contains billions of candidates,and the need to optimize ...Designing refractory high-entropy alloys(RHEAs)for high-temperature(HT)applications is an outstanding challenge given the vast possible composition space,which contains billions of candidates,and the need to optimize across multiple objectives.Here,we present an approach that accelerates the discovery of RHEA compositions with superior strength and ductility by integrating machine learning(ML),genetic search,cluster analysis,and experimental design.We iteratively synthesize and characterize 24 predicted compositions after six feedback loops.Four compositions show outstanding combinations of HT yield strength and room-temperature(RT)ductility spanning the ranges of 714–1061 MPa and 17.2%–50.0%fracture strain,respectively.We identify an attractive alloy system,ZrNbMoHfTa,particularly the composition Zr_(0.13)Nb_(0.27)Mo_(0.26)Hf_(0.13)Ta_(0.21),which demonstrates a yield approaching 940 MPa at 1200℃ and favorable RT ductility with 17.2%fracture strain.The high yield strength at 1200℃ exceeds that reported for RHEAs,with 1200℃ exceeding the service temperature limit for nickel(Ni)-based superalloys.Our ML-based approach makes it possible to rapidly optimize multiple properties for materials design,thus overcoming the common problems of limited data and a vast composition space in complex materials systems while satisfying multiple objectives.展开更多
Low Earth Orbit(LEO)satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently.A few LEO Navigation Augmentation(LEO-...Low Earth Orbit(LEO)satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently.A few LEO Navigation Augmentation(LEO-NA)constellations have been proposed,while corresponding constellation design methodologies have not been systematically studied.The LEO-NA constellation generally consists of a huge number of LEO satellites and it strives for multiple optimization purposes.It is essentially different from the communication constellation or earth observing constellation design problem.In this study,we modeled the LEO-NA constellation design problem as a multi-objective optimization problem and solve this problem with the MultiObjective Particle Swarm Optimization(MOPSO)algorithm.Three objectives are used to strive for the best tradeoff between the augmentation performance and deployment efficiency,namely the Position Dilution of Precision(PDOP),visible LEO satellites and the orbit altitude.A fuzzy set approach is used to select the final constellation from a set of Pareto optimal solutions given by the MOPSO algorithm.To evaluate the performance of the optimized constellation,we tested two constellations with 144 and 288 satellites and each constellation has three optimization schemes:the polar constellation,the single-layer constellation and the two-layer constellation.The results indicate that the optimized two-layer constellation achieves the best global coverage and is followed by the single-layer constellation.The MOPSO algorithm can help to improve the constellation design and is suitable for solving the LEO-NA constellation design problem.展开更多
Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controll...Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).展开更多
基金National Natural Science Foundation of China under Grant No.52278490。
文摘Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.
基金This work was supported in part by the National Natural Science Foundation of China under Grant51507016。
文摘For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.
基金Supported by National Natural Science Foundation of China(Grant No.51375363)
文摘Scanning ion conductance microscopy（SICM） is an emerging non-destructive surface topography characterization apparatus with nanoscale resolution. However, the low regulating frequency of probe in most existing modulated current based SICM systems increases the system noise, and has difficulty in imaging sample surface with steep height changes. In order to enable SICM to have the capability of imaging surfaces with steep height changes, a novel probe that can be used in the modulated current based bopping mode is designed. The design relies on two piezoelectric ceramics with different travels to separate position adjustment and probe frequency regulation in the Z direction. To fiarther improve the resonant frequency of the probe, the material and the key dimensions for each component of the probe are optimized based on the multi-objective optimization method and the finite element analysis. The optimal design has a resonant frequency of above 10 kHz. To validate the rationality of the designed probe, microstructured grating samples are imaged using the homebuilt modulated current based SICM system. The experimental results indicate that the designed high frequency probe can effectively reduce the spike noise by 26% in the average number of spike noise. The proposed design provides a feasible solution for improving the imaging quality of the existing SICM systems which normally use ordinary probes with relatively low regulating frequency.
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
基金funded by Researchers Supporting Programnumber(RSPD2024R809),King Saud University,Riyadh,Saudi Arabia.
文摘Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222505,52321002)Shanghai Municipal Natural Science Foundation o China(Grant No.23ZR1415500)。
文摘Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.
文摘A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.
基金National Science Fund for Distinguished Young Scholars (10425208)Programme of Introducing Talents of Discipline to Universities (B07009)
文摘For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.
文摘Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many man- ufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of mul- tiple objective functions related to process performance and cost is necessary. In this work, a multi- objective optimal experimental design framework is proposed to enhance the ef ciency of online model-identi cation platforms. The proposed framework permits exibility in the choice of trade-off experimental design solutions, which are calculated online that is, during the execution of experiments. The application of this framework to improve the online identi cation of kinetic models in ow reactors is illustrated using a case study in which a kinetic model is identi ed for the esteri cation of benzoic acid and ethanol in a microreactor.
基金supported by the UC MEXUSCONACyT("Cell-to-cell Mapping for Global Multi-objective Optimization")the National Natural Science Foundation of China(11172197)+1 种基金the Natural Science Foundation of Tianjin through a key-project grantsupport from CONACyT through a scholarship to pursue graduate studies at the Computer Science Department of CINVESTAV-IPN
文摘It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design. @ 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi：10.1063/2.1306306]
基金partly supported by the Natural Science Foundation of Guangdong (No.06023131)
文摘This paper proposes a new type of nonlinear controllers and a large phase angle allowance design method based on the multi-objective optimal control system. With the proposed method, the performance of the system becomes better than that of the original system. Then, an example of the radar servo system is designed with a large phase angle allowance multi-objective optimal design method. Finally, the performance based on computer simulation demonstrates that the multi-objective optimal system is superior to linear optimal systems.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the Education and Teaching Research Project of Universities in Fujian Province(FBJY20230167).
文摘The work takes a new liquid-cooling plate in a power battery with pin fins inside the channel as the object.A mathematical model is established via the central composite design of the response surface to study the relationships among the length,width,height,and spacing of pin fins;the maximum temperature and temperature difference of the battery module;and the pressure drop of the liquid-cooling plate.Model accuracy is verified via variance analysis.The new liquid-cooling plate enables the power battery to work within an optimal temperature range.Appropriately increasing the length,width,and height and reducing the spacing of pin fins could reduce the temperature of the power battery module and improve the temperature uniformity.However,the pressure drop of the liquid-cooling plate increases.The structural parameters of the pin fins are optimized to minimize the maximum temperature and the temperature difference of the battery module as well as the pressure drop of the liquid-cooling plate.The errors between the values predicted and actual by the simulation test are 0.58%,4%,and 0.48%,respectively,which further verifies the model accuracy.The results reveal the influence of the structural parameters of the pin fins inside the liquid-cooling plate on its heat dissipation performance and pressure drop characteristics.A theoretical basis is provided for the design of liquid-cooling plates in power batteries and the optimization of structural parameters.
基金supported by National Hi-tech Research and Development Program of China(863 Program, Grant No. 2007AA04Z132)National Natural Science Foundation of China(Grant No. 51175379)
文摘Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene（PP） under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment（DOE）, response surface model（RSM）, and collaborative optimization（CO） are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm（MOGA）, the optimal Pareto sets are completed to solve the multi-objective optimization（MOO） problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.
基金financial support of the National Key Research and Development Program of China(2021YFB3802100)the National Natural Science Foundation of China(52203293)the Innovation Centre of Nuclear Materials Fund(ICNM-2022-ZH-02).
文摘Designing refractory high-entropy alloys(RHEAs)for high-temperature(HT)applications is an outstanding challenge given the vast possible composition space,which contains billions of candidates,and the need to optimize across multiple objectives.Here,we present an approach that accelerates the discovery of RHEA compositions with superior strength and ductility by integrating machine learning(ML),genetic search,cluster analysis,and experimental design.We iteratively synthesize and characterize 24 predicted compositions after six feedback loops.Four compositions show outstanding combinations of HT yield strength and room-temperature(RT)ductility spanning the ranges of 714–1061 MPa and 17.2%–50.0%fracture strain,respectively.We identify an attractive alloy system,ZrNbMoHfTa,particularly the composition Zr_(0.13)Nb_(0.27)Mo_(0.26)Hf_(0.13)Ta_(0.21),which demonstrates a yield approaching 940 MPa at 1200℃ and favorable RT ductility with 17.2%fracture strain.The high yield strength at 1200℃ exceeds that reported for RHEAs,with 1200℃ exceeding the service temperature limit for nickel(Ni)-based superalloys.Our ML-based approach makes it possible to rapidly optimize multiple properties for materials design,thus overcoming the common problems of limited data and a vast composition space in complex materials systems while satisfying multiple objectives.
基金the National Natural Science Foundation of China(Nos.41704002,91638203,41904038)。
文摘Low Earth Orbit(LEO)satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently.A few LEO Navigation Augmentation(LEO-NA)constellations have been proposed,while corresponding constellation design methodologies have not been systematically studied.The LEO-NA constellation generally consists of a huge number of LEO satellites and it strives for multiple optimization purposes.It is essentially different from the communication constellation or earth observing constellation design problem.In this study,we modeled the LEO-NA constellation design problem as a multi-objective optimization problem and solve this problem with the MultiObjective Particle Swarm Optimization(MOPSO)algorithm.Three objectives are used to strive for the best tradeoff between the augmentation performance and deployment efficiency,namely the Position Dilution of Precision(PDOP),visible LEO satellites and the orbit altitude.A fuzzy set approach is used to select the final constellation from a set of Pareto optimal solutions given by the MOPSO algorithm.To evaluate the performance of the optimized constellation,we tested two constellations with 144 and 288 satellites and each constellation has three optimization schemes:the polar constellation,the single-layer constellation and the two-layer constellation.The results indicate that the optimized two-layer constellation achieves the best global coverage and is followed by the single-layer constellation.The MOPSO algorithm can help to improve the constellation design and is suitable for solving the LEO-NA constellation design problem.
基金supported in part by the Scientific Research Fund of National Natural Science Foundation of China(Grant No.62372168)the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ30266)+2 种基金the Research Project on teaching reform in Hunan province(No.HNJG-2022-0791)the Hunan University of Science and Technology(No.2022-44-8)the National Social Science Funds of China(19BZX044).
文摘Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).
