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
A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and metho...A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.展开更多
There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each gam...There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.展开更多
In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory opti...In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory optimization (MORTO) approach via generalized varying domain (GVD) is proposed. Using the direct collocation approach, the trajectory optimization problem involving multiple objectives is discretized into a nonlinear multi-objective programming with priorities. In terms of fuzzy sets, the objectives are fuzzified into three types of fuzzy goals, and their constant tolerances are substituted by the varying domains. According to the principle that the objective with higher priority has higher satisfactory degree, the priority requirement is modeled as the order constraints of the varying domains. The corresponding two-side, single-side, and hybrid-side varying domain models are formulated for three fuzzy relations respectively. By regulating the parameter, the optimal reentry trajectory satisfying priorities can be achieved. Moreover, the performance about the parameter is analyzed, and the algorithm to find its specific value for maximum priority difference is proposed. The simulations demonstrate the effectiveness of the proposed method for hypersonic vehicles, and the comparisons with the traditional methods and sensitivity analysis are presented.展开更多
In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algor...In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.展开更多
The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command La...The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.展开更多
To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver co...To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver constraint system. The comparison of the driver mechanics index of the experimental data with the simulation data in the frontal crash shows that the accuracy of simulation model meets the requirements. The optimal Latin test design is adopted, and the global sensitivity analysis of the design parameters is carried out based on the Kriging model. The four most sensitive parameters are selected, and the parameters are solved by a multi-island genetic algorithm.And then the nonlinear programming quadratic line(NLPQL) algorithm is used to search for accurate optimization. The optimal parameters of the occupant restraint system are determined: the limiting force value of force limiter 2 985.603 N, belt extension 12.684%, airbag point explosion time 27.585 ms, and airbag vent diameter 27.338 mm, with the weighted injury criterion(WIC) decreased by 12.97%, the head injury decreased by 22.60%, and the chest compression decreased by 7.29%. The results show that the system integration of passive safety devices such as seat belts and airbags can effectively protect the driver.展开更多
The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo...The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.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 supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayto...The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.展开更多
A356alloy was used as the base metal to produce boron carbide(B4C)/A356composites using friction stir processing(FSP).The microstructural and mechanical properties of B4C/A356composites were optimized using artificial...A356alloy was used as the base metal to produce boron carbide(B4C)/A356composites using friction stir processing(FSP).The microstructural and mechanical properties of B4C/A356composites were optimized using artificial neural network(ANN)and non-dominated sorting genetic algorithm-II(NSGA-II).Firstly,microstructural properties of the composites fabricated in different processing conditions were investigated.Results show that FSP parameters such as rotational speed,traverse speed and tool pin profile significantly affect the size of the primary silicon(Si)particles of the base metal,as well as the dispersion quality and volume fraction of reinforcing B4C particles in the composite layer.Higher rotational to traverse speeds ratio accompanied by threaded pin profile leads to better particles distribution,finer Si particles and smaller B4C agglomerations.Secondly,hardness and tensile tests were performed to study mechanical properties of the composites.FSP changes the fracture mechanism from brittle form in the as-received metal to very ductile form in the FSPed specimens.Then,a relation between the FSP parameters and microstructural and mechanical properties of the composites was established using ANN.A modified NSGA-II by incorporating diversity preserving mechanism called theεelimination algorithm was employed to obtain the Pareto-optimal set of FSP parameters.展开更多
A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation(DNSGA-II)strategy is proposed to make the product appearance optimization ...A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation(DNSGA-II)strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product.First,the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users;then,the product multidimensional scale analysis is applied to classify the research objects,and again the reference samples are screened by the semantic differentialmethod,and the samples are parametrized in two dimensions by using elliptic Fourier analysis;finally,the fuzzy dynamic evaluation function is used as the objective function of the algorithm,and the coordinates of key points of product contours Finally,with the fuzzy dynamic evaluation function as the objective function of the algorithm and the coordinates of key points of the product profile as the decision variables,the optimal product profile solution set is solved by DNSGA-II.The validity of the model is verified by taking the optimization of the shape scheme of the hospital connection site as an example.For comparison with DNSGA-II,other multi-objective optimization algorithms are also presented.To evaluate the performance of each algorithm,the performance evaluation index values of the five multi-objective optimization algorithms are calculated in this paper.The results show that DNSGA-II is superior in improving individual diversity and has better overall performance.展开更多
In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basi...In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basic random variables are known under condition. It used the Ant Colony Algorithm to design cutting head roadheader, the optimized result indicated that cutting head load fluctuation and compared energy consumption were reduced obviously at the same time. This result enhanced roadheader operational reliability and energy effectively.展开更多
For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization...For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors,while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective.Based on this,the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model.The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method(APCK for short),which is then integrated with the SORA optimisation algorithm.The objective function is the lightweight of gear pair,the maximum overlap degree and the maximum anti-glue strength;the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables;the amount of thermal deformation and thermal resonance,as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints;the optimisation results show that:the mass of the gear is reduced by 0.13kg,the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods,the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting,ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.展开更多
Rotor airfoil design is investigated in this paper. There are many difficulties for this highdimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer...Rotor airfoil design is investigated in this paper. There are many difficulties for this highdimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer hierarchical constraint method is proposed by coupling principal component analysis(PCA) dimensionality reduction and e-constraint method to translate the original high-dimensional problem into a bi-objective problem. This paper selects the main design objectives by conducting PCA to the preliminary solution of original problem with consideration of the priority of design objectives. According to the e-constraint method, the design model is established by treating the two top-ranking design goals as objective and others as variable constraints. A series of bi-objective Pareto curves will be obtained by changing the variable constraints, and the favorable solution can be obtained by analyzing Pareto curve spectrum. This method is applied to the rotor airfoil design and makes great improvement in aerodynamic performance. It is shown that the method is convenient and efficient, beyond which, it facilitates decision-making of the highdimensional multi-objective engineering problem.展开更多
The shape approximation method has been proven to be rapid and practicable in resolving low-thrust trajectory;however,it still faces the challenges of large deviation from the optimal solution and inability to satisfy...The shape approximation method has been proven to be rapid and practicable in resolving low-thrust trajectory;however,it still faces the challenges of large deviation from the optimal solution and inability to satisfy the specific flight time and fuel mass constraints.In this paper,a modified shape approximation low-thrust model is presented,and a novel constrained optimization algorithm is developed to solve this problem.The proposed method aims at settling the bi-objective optimization orbit involving the twin objectives of minimum flight time and low fuel consumption and enhancing the accuracy of optimized orbit.In particular,a transformed high-order polynomial model based on finite Fourier series is proposed,which can be characterized as a multi-constraint optimization problem.Then,a novel optimization algorithm is specifically developed to optimize the large-scale multi-constraint dynamical equations of shape trajectory.The key performance indicators of the index include minimum flight time,low fuel consumption and bi-objective optimization of the two.Simulation results prove that this approach possesses both the high precision achievable by numerical methods and low computational complexity offered by shape approximation techniques.Besides,the Pareto front of the fuel-time bi-objective optimization orbit is firstly introduced to analyze an intact optimal solution set.Furthermore,we have demonstrated that our proposed approach is appropriate to generate the preliminary orbit for pseudo-spectral method.展开更多
Constrained reentry trajectory optimization for hypersonic vehicles is a challenging job. In particular, this problem becomes more difficult when several objectives with preemptive priorities are expected for differen...Constrained reentry trajectory optimization for hypersonic vehicles is a challenging job. In particular, this problem becomes more difficult when several objectives with preemptive priorities are expected for different purposes. In this paper, a fuzzy satisfactory goal programming method is proposed to solve the multi-objective reentry trajectory optimization problem. Firstly, direct collocation approach is used to discretize the reentry trajectory optimal-control problem with nonlinear constraints into nonlinear multiobjective programming problem with preemptive priorities, where attack angles and bank angles at nodes and collocation nodes are selected as control variables. Secondly, the preemptive priorities are transformed into the relaxed order of satisfactory degrees according to the principle that the objective with higher priority has higher satisfactory degree. Then the fuzzy satisfactory goal programming model is proposed. The balance between optimization and priorities is realized by regulating parameter λ, such that the satisfactory reentry trajectory can be acquired. The simulation demonstrates that the proposed method is effective for the multi-objective reentry trajectory optimization of hypersonic vehicles.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
As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimizat...As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.展开更多
The application of new soft magnetic materials in permanent magnet motor can effectively reduce the loss of motor and improve the efficiency of motor. Taguchi method is a local multivariable and multi-objective optimi...The application of new soft magnetic materials in permanent magnet motor can effectively reduce the loss of motor and improve the efficiency of motor. Taguchi method is a local multivariable and multi-objective optimization method widely used in various engineering problems, which can effectively improve the efficiency of engineering optimization. In this paper, based on a 25 kW, 1700 r/min three-phase permanent magnet motor, the relevant motor model is established in the finite element simulation software, and the relevant simulation analysis is carried out. Combined with Taguchi method optimization, the local optimal structure scheme is obtained. Through optimization, the motor can maintain high efficiency, reduce the cogging torque of the motor by 53.45%, reduce the torque ripple by 36.79%, and increase the torque generated by the permanent magnet per unit mass by 21.42%. Through this optimization, the overall performance of the motor has been significantly improved. The research content of this paper verifies the feasibility of the application of Taguchi method in the optimization of new soft magnetic material motor, provides a new idea for the optimization design of new soft magnetic material motor, and also provides a certain reference for the local multi-objective optimization of the electromagnetic structure of other similar motors.展开更多
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 National Natural Science Foundation of China(Grant No:52271300,52071337)National Key Research and Development Program of China(2022YFC2806501)+1 种基金High-tech Ship Research Projects Sponsored by MIIT(CBG2N21-4-25)Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT14R58).
文摘A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.
基金The project supported by the National Natural Science Foundation of China (10372040)Scientific Research Foundation (SRF) for Returned Oversea's Chinese Scholars (ROCS) (2003-091). The English text was polished by Yunming Chen
文摘There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.
基金supported by the Natural Science Foundation of Tianjin(12JCZDJC30300)the Research Foundation of Tianjin Key Laboratory of Process Measurement and Control(TKLPMC-201613)the State Scholarship Fund of China
文摘In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory optimization (MORTO) approach via generalized varying domain (GVD) is proposed. Using the direct collocation approach, the trajectory optimization problem involving multiple objectives is discretized into a nonlinear multi-objective programming with priorities. In terms of fuzzy sets, the objectives are fuzzified into three types of fuzzy goals, and their constant tolerances are substituted by the varying domains. According to the principle that the objective with higher priority has higher satisfactory degree, the priority requirement is modeled as the order constraints of the varying domains. The corresponding two-side, single-side, and hybrid-side varying domain models are formulated for three fuzzy relations respectively. By regulating the parameter, the optimal reentry trajectory satisfying priorities can be achieved. Moreover, the performance about the parameter is analyzed, and the algorithm to find its specific value for maximum priority difference is proposed. The simulations demonstrate the effectiveness of the proposed method for hypersonic vehicles, and the comparisons with the traditional methods and sensitivity analysis are presented.
文摘In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.
基金This work was financially supported by the Key Research and Development Project of Shandong Province(Grant No.2020CXGC010702).
文摘The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.
基金Supported by Natural Science and Technology Research Project of the Jiangxi Education Department(GJJ202002, GJJ2202620)。
文摘To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver constraint system. The comparison of the driver mechanics index of the experimental data with the simulation data in the frontal crash shows that the accuracy of simulation model meets the requirements. The optimal Latin test design is adopted, and the global sensitivity analysis of the design parameters is carried out based on the Kriging model. The four most sensitive parameters are selected, and the parameters are solved by a multi-island genetic algorithm.And then the nonlinear programming quadratic line(NLPQL) algorithm is used to search for accurate optimization. The optimal parameters of the occupant restraint system are determined: the limiting force value of force limiter 2 985.603 N, belt extension 12.684%, airbag point explosion time 27.585 ms, and airbag vent diameter 27.338 mm, with the weighted injury criterion(WIC) decreased by 12.97%, the head injury decreased by 22.60%, and the chest compression decreased by 7.29%. The results show that the system integration of passive safety devices such as seat belts and airbags can effectively protect the driver.
基金Project(61801495)supported by the National Natural Science Foundation of China
文摘The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.
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.
基金This work was supported of National Natural Science Foundation of China Fund(No.52306033)State Key Laboratory of Engines Fund(No.SKLE-K2022-07)the Jiangxi Provincial Postgraduate Innovation Special Fund(No.YC2022-s513).
文摘The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.
文摘A356alloy was used as the base metal to produce boron carbide(B4C)/A356composites using friction stir processing(FSP).The microstructural and mechanical properties of B4C/A356composites were optimized using artificial neural network(ANN)and non-dominated sorting genetic algorithm-II(NSGA-II).Firstly,microstructural properties of the composites fabricated in different processing conditions were investigated.Results show that FSP parameters such as rotational speed,traverse speed and tool pin profile significantly affect the size of the primary silicon(Si)particles of the base metal,as well as the dispersion quality and volume fraction of reinforcing B4C particles in the composite layer.Higher rotational to traverse speeds ratio accompanied by threaded pin profile leads to better particles distribution,finer Si particles and smaller B4C agglomerations.Secondly,hardness and tensile tests were performed to study mechanical properties of the composites.FSP changes the fracture mechanism from brittle form in the as-received metal to very ductile form in the FSPed specimens.Then,a relation between the FSP parameters and microstructural and mechanical properties of the composites was established using ANN.A modified NSGA-II by incorporating diversity preserving mechanism called theεelimination algorithm was employed to obtain the Pareto-optimal set of FSP parameters.
基金supported by National Natural Science Foundation Grant 52065010the Science and Technology Project supported by Guizhou Province of China ZK[2021]341 and[2021]397the transformation Project of Scientific and Technological Achievements in Guiyang,Guizhou Province,China[2021]7-3.
文摘A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation(DNSGA-II)strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product.First,the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users;then,the product multidimensional scale analysis is applied to classify the research objects,and again the reference samples are screened by the semantic differentialmethod,and the samples are parametrized in two dimensions by using elliptic Fourier analysis;finally,the fuzzy dynamic evaluation function is used as the objective function of the algorithm,and the coordinates of key points of product contours Finally,with the fuzzy dynamic evaluation function as the objective function of the algorithm and the coordinates of key points of the product profile as the decision variables,the optimal product profile solution set is solved by DNSGA-II.The validity of the model is verified by taking the optimization of the shape scheme of the hospital connection site as an example.For comparison with DNSGA-II,other multi-objective optimization algorithms are also presented.To evaluate the performance of each algorithm,the performance evaluation index values of the five multi-objective optimization algorithms are calculated in this paper.The results show that DNSGA-II is superior in improving individual diversity and has better overall performance.
文摘In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basic random variables are known under condition. It used the Ant Colony Algorithm to design cutting head roadheader, the optimized result indicated that cutting head load fluctuation and compared energy consumption were reduced obviously at the same time. This result enhanced roadheader operational reliability and energy effectively.
基金financed with the means of Yingkou Institute of Technology Introduction of doctors to start the fund project (YJRC202109).
文摘For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors,while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective.Based on this,the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model.The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method(APCK for short),which is then integrated with the SORA optimisation algorithm.The objective function is the lightweight of gear pair,the maximum overlap degree and the maximum anti-glue strength;the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables;the amount of thermal deformation and thermal resonance,as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints;the optimisation results show that:the mass of the gear is reduced by 0.13kg,the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods,the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting,ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.
基金supported by the National Natural Science Foundation of China (No. 11402288 and 11372254)the National Basic Research Program of China (No. 2014CB744804)
文摘Rotor airfoil design is investigated in this paper. There are many difficulties for this highdimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer hierarchical constraint method is proposed by coupling principal component analysis(PCA) dimensionality reduction and e-constraint method to translate the original high-dimensional problem into a bi-objective problem. This paper selects the main design objectives by conducting PCA to the preliminary solution of original problem with consideration of the priority of design objectives. According to the e-constraint method, the design model is established by treating the two top-ranking design goals as objective and others as variable constraints. A series of bi-objective Pareto curves will be obtained by changing the variable constraints, and the favorable solution can be obtained by analyzing Pareto curve spectrum. This method is applied to the rotor airfoil design and makes great improvement in aerodynamic performance. It is shown that the method is convenient and efficient, beyond which, it facilitates decision-making of the highdimensional multi-objective engineering problem.
基金supported by the National Natural Science Foundation of China(Nos.61627810,61790562,61403096).
文摘The shape approximation method has been proven to be rapid and practicable in resolving low-thrust trajectory;however,it still faces the challenges of large deviation from the optimal solution and inability to satisfy the specific flight time and fuel mass constraints.In this paper,a modified shape approximation low-thrust model is presented,and a novel constrained optimization algorithm is developed to solve this problem.The proposed method aims at settling the bi-objective optimization orbit involving the twin objectives of minimum flight time and low fuel consumption and enhancing the accuracy of optimized orbit.In particular,a transformed high-order polynomial model based on finite Fourier series is proposed,which can be characterized as a multi-constraint optimization problem.Then,a novel optimization algorithm is specifically developed to optimize the large-scale multi-constraint dynamical equations of shape trajectory.The key performance indicators of the index include minimum flight time,low fuel consumption and bi-objective optimization of the two.Simulation results prove that this approach possesses both the high precision achievable by numerical methods and low computational complexity offered by shape approximation techniques.Besides,the Pareto front of the fuel-time bi-objective optimization orbit is firstly introduced to analyze an intact optimal solution set.Furthermore,we have demonstrated that our proposed approach is appropriate to generate the preliminary orbit for pseudo-spectral method.
基金supported by National Natural Science Foundation of China(No.61074064)Natural Science Foundation of Tianjin(No.12JCZDJC30300)
文摘Constrained reentry trajectory optimization for hypersonic vehicles is a challenging job. In particular, this problem becomes more difficult when several objectives with preemptive priorities are expected for different purposes. In this paper, a fuzzy satisfactory goal programming method is proposed to solve the multi-objective reentry trajectory optimization problem. Firstly, direct collocation approach is used to discretize the reentry trajectory optimal-control problem with nonlinear constraints into nonlinear multiobjective programming problem with preemptive priorities, where attack angles and bank angles at nodes and collocation nodes are selected as control variables. Secondly, the preemptive priorities are transformed into the relaxed order of satisfactory degrees according to the principle that the objective with higher priority has higher satisfactory degree. Then the fuzzy satisfactory goal programming model is proposed. The balance between optimization and priorities is realized by regulating parameter λ, such that the satisfactory reentry trajectory can be acquired. The simulation demonstrates that the proposed method is effective for the multi-objective reentry trajectory optimization of hypersonic vehicles.
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supported by the National Natural Science Foundation of China(60873099)the Fundamental Research Funds for the Central Universities(2011QNA29)
文摘As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.
文摘The application of new soft magnetic materials in permanent magnet motor can effectively reduce the loss of motor and improve the efficiency of motor. Taguchi method is a local multivariable and multi-objective optimization method widely used in various engineering problems, which can effectively improve the efficiency of engineering optimization. In this paper, based on a 25 kW, 1700 r/min three-phase permanent magnet motor, the relevant motor model is established in the finite element simulation software, and the relevant simulation analysis is carried out. Combined with Taguchi method optimization, the local optimal structure scheme is obtained. Through optimization, the motor can maintain high efficiency, reduce the cogging torque of the motor by 53.45%, reduce the torque ripple by 36.79%, and increase the torque generated by the permanent magnet per unit mass by 21.42%. Through this optimization, the overall performance of the motor has been significantly improved. The research content of this paper verifies the feasibility of the application of Taguchi method in the optimization of new soft magnetic material motor, provides a new idea for the optimization design of new soft magnetic material motor, and also provides a certain reference for the local multi-objective optimization of the electromagnetic structure of other similar motors.
