The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelli...In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)w...The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.展开更多
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres...Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution l...The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,m...Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,making it crucial to enhance the anchoring performance of rock bolts.First,the stress state of the anchor rod under axial loading across five stages of any anchored segment is analyzed.The shear stress patterns at the anchoring interface during different stages are elucidated.A refined mechanical model of the anchoring interface incorporating surface rib parameters is established.A failure criterion for the anchoring interface under the influence of ground temperature or groundwater is derived and validated.Second,the influence of anchor rib parameters on anchoring force is abalyzed,and in-situ shear tests are conducted.Results indicate that increasing the rib angle and optimizing rib spacing can enhance anchoring force.To minimize the shear component of axial force at the anchor interface,the rib angle of the anchor bolt should not be less than 70°.When the anchor grout possesses high inherent strength,the spacing between ribs on the anchor bolt surface may be increased(to 24 mm or greater).Finally,methods for enhancing the anchoring performance of bolts in deep complex strata are proposed,providing technical references for the safe and efficient support of tunnel rock masses in similar geological conditions.展开更多
Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the opt...Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the optimal collaborative control parameters that support rapid drilling is crucial for improving the combined performance.This study used average drilling speed,average torque,and total specificenergy for quantitative analysis to characterize the efficiencyand economy of combined rock breaking.Given the advantage of the response surface methodology in providing high-precision predictions with limited experimental data,regression models of the average drilling speed,average torque,and total specificenergy were established.The results showed that as the laser power and irradiation time increased,the average drilling speed firstincreased rapidly and then leveled off,while the average torque decreased sharply before decelerating.The total specificenergy initially decreased and then increased,with the combined drilling outperforming conventional mechanical drilling within specific parameter ranges.As the weight on bit increased,both the average torque and total specificenergy first decreased and then increased.With rising rotating speed,the average torque exhibited a trend of initial increase,then decrease,and finalincrease,whereas the total specificenergy increased slowly at firstand then sharply.Both parameters exhibited optimal values at which the average torque and total specific energy remained at minimal levels.For granite combined drilling,the optimal performance was achieved at a laser power of 3000 W,irradiation time of 31 s,the weight on bit of 2.4 kN,and the rotating speed of 97 r/min.展开更多
Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong ad...Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong adaptability for driving cycles. Never- theless, for configuring the PHEB with single-shaft series-parallel powertrain in the development stage, it still faces greater challenge than other configurations when choosing and matching the main component parameters. Motivated by this issue, a comprehensive multi-objectives optimization strategy based on Genetic Algorithm （GA） is developed for the PHEB with the typical powertrain. First, considering repeatability and regularity of bus route, the methods of off-line data processing and mathematical statistics are adopted, to obtain a representative driving cycle, which could well reflect the general characteristic of the real-world bus route. Then, the economical optimization objective is defined, which is consist of manufacturing costs of the key components and energy consumption, and combined with the dynamical optimization objective, a multi-objective op- timization function is put forward. Meanwhile, GA algorithm is used to optimize the parameters, for the optimal components combination of the novel series-parallel powertrain. Finally, a comparison with the prototype is carried out to verify the per- formance of the optimized powertrain along driving cycles. Simulation results indicate that the parameters of powertrain com- ponents obtained by the proposed comprehensive multi-objectives optimization strategy might get better fuel economy, meanwhile ensure the dynamic performance of PHEB. In contrast to the original, the costs declined by 18%. Hence, the strat- egy would provide a theoretical guidance on parameter selection for PHEB manufacturers.展开更多
Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosi...Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.展开更多
Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design r...Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.展开更多
This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi's method,a process analysis by analysis of vari...This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi's method,a process analysis by analysis of variance(ANOVA),a process modeling algorithm by artificial neural network(ANN),and a multi-objective parameter optimization algorithm by genetic algorithm(GA)-based lexicographic method.Local and global Pareto analyses show the trade-off between product quality and energy consumption.The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests,and at the same time,the product quality can meet the pre-determined requirements.展开更多
The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input par...The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation.展开更多
Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness i...Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness in noise scenarios.To address these limitations,we aim to develop a robust,efficient and globally convergent solver for nonconvex optimisation.This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics(PLND)model with the global search capability of the coevolutionary mechanism.We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters(CNDLP)model.The gradient information is used to find the optimal solution more effectively,and neural dynamics models have robustness,which ensures that the influence of noise can be effectively suppressed in the calculation process.Theoretical analyses show the global convergence and robustness of the designed CNDLP model.Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms.Benchmarks cover diverse problems,from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock.Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.展开更多
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金supported by the National Natural Science Foundation of China(Grant No.52179105)China Postdoctoral Science Foundation(Grant No.2024M762193)。
文摘In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
基金supported by the National Science and Technology Council,Taiwan,under Grants 113-2221-E-260-014-MY2 and 114-2119-M-033-001.
文摘The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.
基金supported by the National Natural Science Foundation of China(No.62173107).
文摘Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
文摘The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
基金The Natural Science Research Project of Anhui Educational Committee(No.2022AH050814)Open Fund of State Key Laboratory of Nuclear Resources and Environment(East China Universityof Technology)(No.2022NRE07)+1 种基金the National Natural Science Foundation of China(No.51964002,52174104)Open Fund of Engineering Research Center of Underground Mine Construction of Ministry of Education(No.JYBGCZX2022105).
文摘Deep coal mining rock support structures using rock bolts face complex geological conditions such as high ground temperatures and groundwater.Rock mass deformation and failure caused by bolt failure frequently occur,making it crucial to enhance the anchoring performance of rock bolts.First,the stress state of the anchor rod under axial loading across five stages of any anchored segment is analyzed.The shear stress patterns at the anchoring interface during different stages are elucidated.A refined mechanical model of the anchoring interface incorporating surface rib parameters is established.A failure criterion for the anchoring interface under the influence of ground temperature or groundwater is derived and validated.Second,the influence of anchor rib parameters on anchoring force is abalyzed,and in-situ shear tests are conducted.Results indicate that increasing the rib angle and optimizing rib spacing can enhance anchoring force.To minimize the shear component of axial force at the anchor interface,the rib angle of the anchor bolt should not be less than 70°.When the anchor grout possesses high inherent strength,the spacing between ribs on the anchor bolt surface may be increased(to 24 mm or greater).Finally,methods for enhancing the anchoring performance of bolts in deep complex strata are proposed,providing technical references for the safe and efficient support of tunnel rock masses in similar geological conditions.
基金funded by the National Natural Science Foundation of China(Grand No.52325904)National Key Research and Development Program of China(Grant No.2023YFB2390200)the National Natural Science Foundation of China(Grant No.52309134).
文摘Laser-assisted drilling combined with full-size polycrystalline diamond compact(PDC)bit is considered a feasible solution to enhance the drilling performance of engineering machinery.In this method,determining the optimal collaborative control parameters that support rapid drilling is crucial for improving the combined performance.This study used average drilling speed,average torque,and total specificenergy for quantitative analysis to characterize the efficiencyand economy of combined rock breaking.Given the advantage of the response surface methodology in providing high-precision predictions with limited experimental data,regression models of the average drilling speed,average torque,and total specificenergy were established.The results showed that as the laser power and irradiation time increased,the average drilling speed firstincreased rapidly and then leveled off,while the average torque decreased sharply before decelerating.The total specificenergy initially decreased and then increased,with the combined drilling outperforming conventional mechanical drilling within specific parameter ranges.As the weight on bit increased,both the average torque and total specificenergy first decreased and then increased.With rising rotating speed,the average torque exhibited a trend of initial increase,then decrease,and finalincrease,whereas the total specificenergy increased slowly at firstand then sharply.Both parameters exhibited optimal values at which the average torque and total specific energy remained at minimal levels.For granite combined drilling,the optimal performance was achieved at a laser power of 3000 W,irradiation time of 31 s,the weight on bit of 2.4 kN,and the rotating speed of 97 r/min.
基金supported by the National Key Science and Technology Projects(Grant No.2014ZX04002041)
文摘Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong adaptability for driving cycles. Never- theless, for configuring the PHEB with single-shaft series-parallel powertrain in the development stage, it still faces greater challenge than other configurations when choosing and matching the main component parameters. Motivated by this issue, a comprehensive multi-objectives optimization strategy based on Genetic Algorithm （GA） is developed for the PHEB with the typical powertrain. First, considering repeatability and regularity of bus route, the methods of off-line data processing and mathematical statistics are adopted, to obtain a representative driving cycle, which could well reflect the general characteristic of the real-world bus route. Then, the economical optimization objective is defined, which is consist of manufacturing costs of the key components and energy consumption, and combined with the dynamical optimization objective, a multi-objective op- timization function is put forward. Meanwhile, GA algorithm is used to optimize the parameters, for the optimal components combination of the novel series-parallel powertrain. Finally, a comparison with the prototype is carried out to verify the per- formance of the optimized powertrain along driving cycles. Simulation results indicate that the parameters of powertrain com- ponents obtained by the proposed comprehensive multi-objectives optimization strategy might get better fuel economy, meanwhile ensure the dynamic performance of PHEB. In contrast to the original, the costs declined by 18%. Hence, the strat- egy would provide a theoretical guidance on parameter selection for PHEB manufacturers.
基金financially supported by the National Key Research and Development Program of China(Grant No.2016YFB0701204)
文摘Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.
基金financially sponsored by National Natural Science Foundation of China(No.50975121)Changchun Science and Technology Plan Projects(No.10KZ03)the Plan for Scientific and Technology Development of Jilin Province(No.20150520106JH)
文摘Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.
基金(Nos. 20806040,61073059 and 61034005) supported by the National Natural Science Foundation of China
文摘This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi's method,a process analysis by analysis of variance(ANOVA),a process modeling algorithm by artificial neural network(ANN),and a multi-objective parameter optimization algorithm by genetic algorithm(GA)-based lexicographic method.Local and global Pareto analyses show the trade-off between product quality and energy consumption.The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests,and at the same time,the product quality can meet the pre-determined requirements.
文摘The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation.
基金supported by the Leading Talent of the Qinghai Province Kunlun Talents Programme・High-Level Innovative and Entrepreneurial Talents(QHKLYC-GDCXCY-2024-359).
文摘Nonconvex optimisation plays a crucial role in science and industry.However,existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems,lacking robustness in noise scenarios.To address these limitations,we aim to develop a robust,efficient and globally convergent solver for nonconvex optimisation.This is achieved by combining the efficient local exploitation ability of a parameter-learnt neural dynamics(PLND)model with the global search capability of the coevolutionary mechanism.We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters(CNDLP)model.The gradient information is used to find the optimal solution more effectively,and neural dynamics models have robustness,which ensures that the influence of noise can be effectively suppressed in the calculation process.Theoretical analyses show the global convergence and robustness of the designed CNDLP model.Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta-heuristic algorithms.Benchmarks cover diverse problems,from classical landscapes like benchmark Shubert to high-dimensional cases such as 30-dimensional Rosenbrock.Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.
