Intelligent production is an important development direction in intelligent manufacturing,with intelligent factories playing a crucial role in promoting intelligent production.Flexible job shops,as the main form of in...Intelligent production is an important development direction in intelligent manufacturing,with intelligent factories playing a crucial role in promoting intelligent production.Flexible job shops,as the main form of intelligent factories,constantly face dynamic disturbances during the production process,including machine failures and urgent orders.This paper discusses the basic models and research methods of job shop scheduling,emphasizing the important role of dynamic job shop scheduling and its response schemes in future research.A multi-objective flexible job shop dynamic scheduling mathematical model is established,highlighting its complex and multi-constraint characteristics under different interferences.A classification discussion is conducted on the dynamic response methods and optimization objectives under machine failures,emergency orders,fuzzy completion times,and mixed dynamic events.The development process of traditional scheduling rules and intelligent methods in dynamic scheduling are also analyzed.Finally,based on the current development status of job shop scheduling and the requirements of intelligent manufacturing,the future development trends of dynamic scheduling in flexible job shops are proposed.展开更多
In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And th...In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.展开更多
To reduce thrust ripple and cost and improve the average thrust of permanent magnet linear motors,a modular dual-field modulation permanent magnet linear motor was studied,and the parameters were optimized.First,sensi...To reduce thrust ripple and cost and improve the average thrust of permanent magnet linear motors,a modular dual-field modulation permanent magnet linear motor was studied,and the parameters were optimized.First,sensitive parameters were selected using the Taguchi method,and then the optimal variables were sampled using the optimal Latin hypercube experimental design method and an ensemble of surrogates model of optimization objectives,and its accuracy was verified.Next,a multi-objective particle swarm optimization algorithm was used to optimize the purpose of“maximum average thrust and minimum thrust ripple”,and the Pareto front of average thrust and thrust ripple was obtained.Finite element analysis showed that the optimized modular dual flux-modulation permanent magnet linear motor(MDFMPMLM)had a 29.5%reduction in thrust ripple and a 5%increase in average thrust compared to the original motor.This study provided an effective method for improving the performance of permanent magnet linear motors.展开更多
In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In ...In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm opt...Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.展开更多
The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selec...The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selected by the uniform design, and the inlet performance is analyzed by computational fluid dynamics （CFD）. Then complete quadratic polynomial response surface approximation models are constructed based on the performance analysis results and then used to replace theoriginal complex inlet performance model. The optimization is conducted using a multi-objective genetic algorithm NSGA-Ⅱ, and the Pareto optimal solution set is obtained. Results show that the uniform design and RSM can reduce the computational complexity of numerical simulation and improve the optimization efficiency.展开更多
Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularizatio...Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularization inversion. To deal with these problems, we propose a multiobjective particle swarm inversion （MOPSOI） algorithm to simultaneously minimize the data misfit and model constraints, and obtain a multiobjective inversion solution set without the gradient information of the objective function and the regularization factor. We then choose the optimum solution from the solution set based on the trade-off between data misfit and constraints that substitute for the regularization factor. The inversion of synthetic two-dimensional magnetic data suggests that the MOPSOI algorithm can obtain as many feasible solutions as possible; thus, deeper insights of the inversion process can be gained and more reasonable solutions can be obtained by balancing the data misfit and constraints. The proposed MOPSOI algorithm can deal with the problems of choosing the right regularization factor and the initial model.展开更多
Cropping structure has a close relationship with the optimal allocation of agricultural water resources. Based on the analysis of the relationship between agricultural water resources and sustainable development, this...Cropping structure has a close relationship with the optimal allocation of agricultural water resources. Based on the analysis of the relationship between agricultural water resources and sustainable development, this paper presents a multi objective fuzzy optimization model for cropping structure and water allocation, which overcomes the shortcoming of current models that only considered the economic objective,and ignored the social and environmental objectives. During the process, a new method named fuzzy deciding weight is developed to decide the objective weight. A case study shows that the model is reliable, the method is simple and objective, and the results are reasonable. This model is useful for agricultural management and sustainable development.展开更多
Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the co...Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.展开更多
The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an...The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.展开更多
A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pit...A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.展开更多
In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objectiv...In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.展开更多
Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary a...Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.展开更多
The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-obje...The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.展开更多
A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed.After the kinematic and dynamic analysis,several advantages of the mechanism were illustrated,which m...A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed.After the kinematic and dynamic analysis,several advantages of the mechanism were illustrated,which made it possible to obtain good dynamic and control performances just through mechanism optimization.Based on the idea of design for control(DFC),a novel kind of multi-objective optimization model was proposed.There were three optimization objectives:the index of inertia,the index describing the dynamic coupling effects and the global condition number.Other indexes to characterize the designing requirements such as the velocity of end-effector,the workspace size,and the first mode natural frequency were regarded as the constraints.The cross-section area and length of the linkages were chosen as the design variables.NSGA-II algorithm was introduced to solve this complex multi-objective optimization problem.Additional criteria from engineering experience were incorporated into the selecting of final parameters among the obtained Pareto solution sets.Finally,experiments were performed to validate the linear dynamic structure and control performances of the optimized mechanisms.A new expression for measuring the dynamic coupling degree with clear physical meaning was proposed.The results show that the optimized mechanism has an approximate decoupled dynamics structure,and each active joint can be regarded as a linear SISO system.The control performances of the linear and nonlinear controllers were also compared.It can be concluded that the optimized mechanism can achieve good control performance only using a linear controller.展开更多
Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed...Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.展开更多
基金supported by the National Key Research and Development Program Project(No.2021YFB3301300).
文摘Intelligent production is an important development direction in intelligent manufacturing,with intelligent factories playing a crucial role in promoting intelligent production.Flexible job shops,as the main form of intelligent factories,constantly face dynamic disturbances during the production process,including machine failures and urgent orders.This paper discusses the basic models and research methods of job shop scheduling,emphasizing the important role of dynamic job shop scheduling and its response schemes in future research.A multi-objective flexible job shop dynamic scheduling mathematical model is established,highlighting its complex and multi-constraint characteristics under different interferences.A classification discussion is conducted on the dynamic response methods and optimization objectives under machine failures,emergency orders,fuzzy completion times,and mixed dynamic events.The development process of traditional scheduling rules and intelligent methods in dynamic scheduling are also analyzed.Finally,based on the current development status of job shop scheduling and the requirements of intelligent manufacturing,the future development trends of dynamic scheduling in flexible job shops are proposed.
文摘In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.
文摘To reduce thrust ripple and cost and improve the average thrust of permanent magnet linear motors,a modular dual-field modulation permanent magnet linear motor was studied,and the parameters were optimized.First,sensitive parameters were selected using the Taguchi method,and then the optimal variables were sampled using the optimal Latin hypercube experimental design method and an ensemble of surrogates model of optimization objectives,and its accuracy was verified.Next,a multi-objective particle swarm optimization algorithm was used to optimize the purpose of“maximum average thrust and minimum thrust ripple”,and the Pareto front of average thrust and thrust ripple was obtained.Finite element analysis showed that the optimized modular dual flux-modulation permanent magnet linear motor(MDFMPMLM)had a 29.5%reduction in thrust ripple and a 5%increase in average thrust compared to the original motor.This study provided an effective method for improving the performance of permanent magnet linear motors.
基金The National Natural Science Foundation of China(No.51036002,51076027)the Key Project of Ministry of Education of China(No.108060)
文摘In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
文摘Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.
文摘The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selected by the uniform design, and the inlet performance is analyzed by computational fluid dynamics （CFD）. Then complete quadratic polynomial response surface approximation models are constructed based on the performance analysis results and then used to replace theoriginal complex inlet performance model. The optimization is conducted using a multi-objective genetic algorithm NSGA-Ⅱ, and the Pareto optimal solution set is obtained. Results show that the uniform design and RSM can reduce the computational complexity of numerical simulation and improve the optimization efficiency.
基金supported by the Natural Science Foundation of China(No.61273179)Department of Education,Science and Technology Research Project of Hubei Province of China(No.D20131206,No.20141304)
文摘Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularization inversion. To deal with these problems, we propose a multiobjective particle swarm inversion （MOPSOI） algorithm to simultaneously minimize the data misfit and model constraints, and obtain a multiobjective inversion solution set without the gradient information of the objective function and the regularization factor. We then choose the optimum solution from the solution set based on the trade-off between data misfit and constraints that substitute for the regularization factor. The inversion of synthetic two-dimensional magnetic data suggests that the MOPSOI algorithm can obtain as many feasible solutions as possible; thus, deeper insights of the inversion process can be gained and more reasonable solutions can be obtained by balancing the data misfit and constraints. The proposed MOPSOI algorithm can deal with the problems of choosing the right regularization factor and the initial model.
文摘Cropping structure has a close relationship with the optimal allocation of agricultural water resources. Based on the analysis of the relationship between agricultural water resources and sustainable development, this paper presents a multi objective fuzzy optimization model for cropping structure and water allocation, which overcomes the shortcoming of current models that only considered the economic objective,and ignored the social and environmental objectives. During the process, a new method named fuzzy deciding weight is developed to decide the objective weight. A case study shows that the model is reliable, the method is simple and objective, and the results are reasonable. This model is useful for agricultural management and sustainable development.
文摘Multi-objective optimization for the optimum shape design is introduced in aerodynamics using the Game theory. Based on the control theory, the employed optimizer and the negative feedback are used to implement the constraints. All the constraints are satisfied implicitly and automatically in the design. Furthermore,the above methodology is combined with a formulation derived from the Game theory to treat multi-point airfoil optimization. Airfoil shapes are optimized according to various aerodynamics criteria. In the symmetric Nash game, each “player” is responsible for one criterion, and the Nash equilibrium provides a solution to the multipoint optimization. Design results confirm the efficiency of the method.
基金Supported by the National Basic Research Program of China ("973" Program)the National Natural Science Foundation of China (60872112, 10805012)+1 种基金the Natural Science Foundation of Zhejiang Province(Z207588)the College Science Research Project of Anhui Province (KJ2008B268)~~
文摘The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.
基金Supported by Program for New Century Excellent Talents in University (070003)the Natural Science Foundation of Anhui Province (070414154)~~
文摘A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.
文摘In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.
文摘Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.
基金Supported by the National High Technology Research and Development Program of China （2008AA042902, 2009AA04Z162）, the Program of Introducing Talents of Discipline to University （B07031） and the National Natural Science Foundation of China （21106129）.
文摘The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.
基金Project(2009AA04Z216) supported in part by the National High Technology Research and Development Program of ChinaProject(2009ZX04013-011) supported by the National Science and Technology Major Program of ChinaProject(20092302120068) supported by the Doctoral Program of Higher Education of China
文摘A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed.After the kinematic and dynamic analysis,several advantages of the mechanism were illustrated,which made it possible to obtain good dynamic and control performances just through mechanism optimization.Based on the idea of design for control(DFC),a novel kind of multi-objective optimization model was proposed.There were three optimization objectives:the index of inertia,the index describing the dynamic coupling effects and the global condition number.Other indexes to characterize the designing requirements such as the velocity of end-effector,the workspace size,and the first mode natural frequency were regarded as the constraints.The cross-section area and length of the linkages were chosen as the design variables.NSGA-II algorithm was introduced to solve this complex multi-objective optimization problem.Additional criteria from engineering experience were incorporated into the selecting of final parameters among the obtained Pareto solution sets.Finally,experiments were performed to validate the linear dynamic structure and control performances of the optimized mechanisms.A new expression for measuring the dynamic coupling degree with clear physical meaning was proposed.The results show that the optimized mechanism has an approximate decoupled dynamics structure,and each active joint can be regarded as a linear SISO system.The control performances of the linear and nonlinear controllers were also compared.It can be concluded that the optimized mechanism can achieve good control performance only using a linear controller.
基金Project(3502Z20179026)supported by Xiamen Science and Technology Project,China。
文摘Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.
