Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an im...Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II(NSGA-II)for solving the MOLA problem by integrating the patch-based,edge growing/decreasing,neighborhood,and constraint steering rules.By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30×30 grid,we find that:when compared to the classical NSGA-II,the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity;the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation;the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection.The better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context.展开更多
This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation...This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.展开更多
Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufact...Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.展开更多
Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-de...Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.展开更多
In view of the high cost of solar thermal power generation in China,it is difficult to realize large-scale production in engineering and industrialization.Non-dominated sorting genetic algorithm II(NSGA-II)is applied ...In view of the high cost of solar thermal power generation in China,it is difficult to realize large-scale production in engineering and industrialization.Non-dominated sorting genetic algorithm II(NSGA-II)is applied to optimize the levelling cost of energy(LCOE)of the solar thermal power generation system in this paper.Firstly,the capacity and generation cost of the solar thermal power generation system are modeled according to the data of several sets of solar thermal power stations which have been put into production abroad.Secondly,the NSGA-II genetic algorithm and particle swarm algorithm are applied to the optimization of the solar thermal power station LCOE respectively.Finally,for the linear Fresnel solar thermal power system,the simulation experiments are conducted to analyze the effects of different solar energy generation capacities,different heat transfer mediums and loan interest rates on the generation price.The results show that due to the existence of scale effect,the greater the capacity of the power station,the lower the cost of leveling and electricity,and the influence of the types of heat storage medium and the loan on the cost of leveling electricity are relatively high.展开更多
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S...In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.展开更多
Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multio...Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multiobjective and multilevel optimization design framework was constructed for the comprehensive navigation performance of ships.CAESES software was utilized as the optimization platform,and nondominated sorting genetic algorithm II(NSGA-II)was used to conduct multiobjective optimization research on the resistance and sea-keeping performance of the ITTC Ship A-2 fishing vessel.Optimization objectives of this study are heave/pitch response amplitude and wave-making resistance.Taking the displacement and the length between perpendiculars as constraints,we optimized the profile of the hull.Analytic hierarchy process(AHP)and technique for order preference by similarity to ideal solution(TOPSIS)were used to sort and select Pareto solutions and determine weight coefficient of each navigation performance objective in the general objective.Finally,the hydrodynamic performance before and after the parametric deformation of the hull was compared.The results show that both the wave-making resistance and heave/pitch amplitude of the optimized hull form are reduced,and the satisfactory optimal hull form is obtained.The results of this study have a certain reference value for the initial stage of multiobjective optimization design of hull form.展开更多
Optimization of cylindrical roller bearings(CRBs)has been performed using a robust design.It ensures that the changes in the objective function,even in the case of variations in design variables during manufacturing,h...Optimization of cylindrical roller bearings(CRBs)has been performed using a robust design.It ensures that the changes in the objective function,even in the case of variations in design variables during manufacturing,have a minimum possible value and do not exceed the upper limit of a desired range of percentage variation.Also,it checks the feasibility of design outcome in presence of manufacturing tolerances in design variables.For any rolling element bearing,a long life indicates a satisfactory performance.In the present study,the dynamic load carrying capacity C,which relates to fatigue life,has been optimized using the robust design.In roller bearings,boundary dimensions(i.e.,bearing outer diameter,bore diameter and width)are standard.Hence,the performance is mainly affected by the internal dimensions and not the bearing boundary dimensions mentioned formerly.In spite of this,besides internal dimensions and their tolerances,the tolerances in boundary dimensions have also been taken into consideration for the robust optimization.The problem has been solved with the elitist non-dominating sorting genetic algorithm(NSGA-II).Finally,for the visualization and to ensure manufacturability of CRB using obtained values,radial dimensions drawing of one of the optimized CRB has been made.To check the robustness of obtained design after optimization,a sensitivity analysis has also been carried out to find out how much the variation in the objective function will be in case of variation in optimized value of design variables.Optimized bearings have been found to have improved life as compared with standard ones.展开更多
The structure parameters of 6-degree of freedom(DOF)vibration isolation platform have a significant effect on its performance.To make the designed vibration isolation platform perform well,non-dominanted sorting genet...The structure parameters of 6-degree of freedom(DOF)vibration isolation platform have a significant effect on its performance.To make the designed vibration isolation platform perform well,non-dominanted sorting genetic algorithm version II(NSGA-II)was applied to optimize its structure based on the transfer matrix method for multibody systems.Firstly,the Jacobian matrix of 6-DOF vibration isolation platform was solved based on kinematics.Secondly,the transfer equation of 6-DOF vibration isolation system was established by the linear transfer matrix method for multibody systems.And the formula of its natural frequency was derived according to the boundary conditions of the system.Thirdly,the manipulability index was constructed based on a dimensionless Jacobian matrix.And a new performance index function was established considering the influence of dynamic isotropic and legs mass.Fourthly,genetic algorithm(GA)and NSGA-II were used to optimize the structure of the 6-DOF vibration isolation platform under the same conditions,respectively.It showed that NSGA-II had higher optimization efficiency,better calculation accuracy and shorter optimization time than that of GA.Finally,NSGA-II was adopted for multi-objective optimization design of 6-DOF vibration isolation platform based on the constraint conditions.Optimal Pareto solutions were obtained,which provides structural parameters for subsequent design work.Therefore,the proposed optimization method and the performance index in this paper provide a theoretical basis for the optimal design of relevant vibration isolation mechanism.展开更多
The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider m...The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated.Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.展开更多
This paper presents a novel general method for computing optimal motions of an industrial robot manipulator （AdeptOne XL robot） in the presence of fixed and oscillating obstacles. The optimization model considers th...This paper presents a novel general method for computing optimal motions of an industrial robot manipulator （AdeptOne XL robot） in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm （NSGA-II） and multi-objective differential evolution （MODE）, have been used for the optimization. Two methods （normalized weighting objective functions and average fitness factor） are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.展开更多
Abstract A transonic, high Reynolds number natural laminar flow airfoil is designed and studied. The γ-θ transition model is combined with the shear stress transport （SST） k-w turbulence model to predict the trans...Abstract A transonic, high Reynolds number natural laminar flow airfoil is designed and studied. The γ-θ transition model is combined with the shear stress transport （SST） k-w turbulence model to predict the transition region for a laminar-turbulent boundary layer. The non-uniform free-form deformation （NFFD） method based on the non-uniform rational B-spline （NURBS） basis function is introduced to the airfoil parameterization. The non-dominated sorting genetic algorithm-II （NSGA-II） is used as the search algo- rithm, and the surrogate model based on the Kriging models is introduced to improve the efficiency of the optimization system. The optimization system is set up based on the above technologies, and the robust design about the uncertainty of the Mach number is carried out for NASA0412 airfoil. The optimized airfoil is analyzed and compared with the original airfoil. The results show that natural laminar flow can be achieved on a supercritical airfoil to improve the aerodynamic characteristic of airfoils.展开更多
Actuator and hydraulic system parameters have great influence on the performance,safety and reliability of the stepless capacity control system of reciprocating compressor.Due to the diversity and complex relationship...Actuator and hydraulic system parameters have great influence on the performance,safety and reliability of the stepless capacity control system of reciprocating compressor.Due to the diversity and complex relationship of parameters,traditional parameters selected and calculated based on feasibility can’t make the system run efficiently,have limitations,and may have adverse effects on the system or compressor.Therefore,taking the spring stiffness of the actuator and the impact velocity of ejection,the inlet oil pressure of the hydraulic system,and the indicated power deviation of the compressor as objective functions,the multi-parameter and multi-objective optimization research of the actuator and hydraulic system with the stepless capacity control system based on non-dominated sorting genetic algorithm II(NSGA-II)is carried out.Based on fuzzy analytic hierarchy process(FAHP),the optimal solution is selected from the Pareto front,and compared with the traditional design value,the result is better than that obtained by the traditional design method.展开更多
The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-af...The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.展开更多
Based on a thing that it is difficult to choose the parameters of active disturbance rejection control for the non-linear ALSTOM gasifier, multi-objective optimization algorithm is applied in the choose of parameters....Based on a thing that it is difficult to choose the parameters of active disturbance rejection control for the non-linear ALSTOM gasifier, multi-objective optimization algorithm is applied in the choose of parameters. Simulation results show that performance tests in load change and coal quality change achieve better dynamic responses and larger scales of rejecting coal quality disturbances. The study provides an alternative to choose parameters for other control schemes of the ALSTOM gasifier.展开更多
In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Mo...In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.展开更多
Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tas...Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.展开更多
Demand response(DR)and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems.However,the uncertain wind power generation(WG)which has anti-peaking characteristic would be h...Demand response(DR)and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems.However,the uncertain wind power generation(WG)which has anti-peaking characteristic would be hard to exert its ability in carbon reduction.This paper introduces DR into traditional unit commitment(UC)strategy and proposes a multi-objective day-ahead optimal scheduling model for wind farm integrated power systems,since incentive-based DR can accommodate excess wind power and can be used as a source of system spinning reserve to alleviate generation side reserve pressure during both peak and valley load periods.Firstly,net load curve is obtained by forecasting load and wind power output.Then,considering the behavior of DR,a day-ahead optimal dispatching scheme is proposed with objectives of minimum generating cost and carbon emission.Non-dominated sorting genetic algorithm-II(NSGA-II)and satisfaction-maximizing method are adopted to solve the multi-objective model with Pareto fronts and eclectic decision obtained.Finally,a case study is carried out to demonstrate that the approach can achieve economic and environmental aims and DR can help to accommodate the wind power.展开更多
In an unmanned aerial vehicle ad-hoc network(UANET),sparse and rapidly mobile unmanned aerial vehicles(UAVs)/nodes can dynamically change the UANET topology.This may lead to UANET service performance issues.In this st...In an unmanned aerial vehicle ad-hoc network(UANET),sparse and rapidly mobile unmanned aerial vehicles(UAVs)/nodes can dynamically change the UANET topology.This may lead to UANET service performance issues.In this study,for planning rapidly changing UAV swarms,we propose a dynamic value iteration network(DVIN)model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function,which enables UAVs/nodes to adapt to novel physical locations.We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method.Simulation results demonstrate that the proposed model significantly reduces the decisionmaking time for UAV/node path planning with a high average success rate.展开更多
Based on analysis of construction and operation of micro integrated energy systems(MIES), this paper presents economic optimization for their configuration and sizing. After presenting typical models for MIES, a resid...Based on analysis of construction and operation of micro integrated energy systems(MIES), this paper presents economic optimization for their configuration and sizing. After presenting typical models for MIES, a residential community MIES is developed by analyzing residential direct energy consumption within a general design procedure. Integrating with available current technologies and local resources, the systematic design considers a prime mover, fed by natural gas, with wind power, photovoltaic generation, and two storage devices serving thermal energy and power to satisfy cooling, heating and electricity demands. Control strategies for MIES also arepresented in this study. Multi-objective formulas are obtained by analyzing annual cost and dumped renewable energy to achieve optimal coordination of energy supply and demand. According to historical load data and the probability distribution of distributed generation output,clustering methods based on K-means and discretization methods are employed to obtain typical scenarios representative of uncertainties. The modified non-dominated sorting genetic algorithm is applied to find the Pareto frontier of the constructed multi-objective formulas. In addition, aiming to explore the Pareto frontier, the dumped energy cost ratio is defined to check the energy balance in different MIES designs and provide decision support for the investors. Finally, simulations and comparision show the appropriateness of the developed model and the applicability of the adopted optimization algorithm.展开更多
文摘Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II(NSGA-II)for solving the MOLA problem by integrating the patch-based,edge growing/decreasing,neighborhood,and constraint steering rules.By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30×30 grid,we find that:when compared to the classical NSGA-II,the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity;the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation;the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection.The better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context.
文摘This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.
基金supported by the National Natural Science Foundation of China(No.62076095)National Key Research and Development Program(No.2022YFB4602104).
文摘Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.
基金This work was supported by the China Scholarship Council Scholarship,the National Key Research and Development Program of China(2017YFB0306400)the National Natural Science Foundation of China(62073069)the Deanship of Scientific Research(DSR)at King Abdulaziz University(RG-48-135-40).
文摘Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.
基金National Natural Science Foundation of China(No.519667013)
文摘In view of the high cost of solar thermal power generation in China,it is difficult to realize large-scale production in engineering and industrialization.Non-dominated sorting genetic algorithm II(NSGA-II)is applied to optimize the levelling cost of energy(LCOE)of the solar thermal power generation system in this paper.Firstly,the capacity and generation cost of the solar thermal power generation system are modeled according to the data of several sets of solar thermal power stations which have been put into production abroad.Secondly,the NSGA-II genetic algorithm and particle swarm algorithm are applied to the optimization of the solar thermal power station LCOE respectively.Finally,for the linear Fresnel solar thermal power system,the simulation experiments are conducted to analyze the effects of different solar energy generation capacities,different heat transfer mediums and loan interest rates on the generation price.The results show that due to the existence of scale effect,the greater the capacity of the power station,the lower the cost of leveling and electricity,and the influence of the types of heat storage medium and the loan on the cost of leveling electricity are relatively high.
文摘In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.
基金the National Natural Science Foundation of China(Nos.51779135 and 51009087)the Natural Science Foundation of Shanghai(No.14ZR1419500)。
文摘Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multiobjective and multilevel optimization design framework was constructed for the comprehensive navigation performance of ships.CAESES software was utilized as the optimization platform,and nondominated sorting genetic algorithm II(NSGA-II)was used to conduct multiobjective optimization research on the resistance and sea-keeping performance of the ITTC Ship A-2 fishing vessel.Optimization objectives of this study are heave/pitch response amplitude and wave-making resistance.Taking the displacement and the length between perpendiculars as constraints,we optimized the profile of the hull.Analytic hierarchy process(AHP)and technique for order preference by similarity to ideal solution(TOPSIS)were used to sort and select Pareto solutions and determine weight coefficient of each navigation performance objective in the general objective.Finally,the hydrodynamic performance before and after the parametric deformation of the hull was compared.The results show that both the wave-making resistance and heave/pitch amplitude of the optimized hull form are reduced,and the satisfactory optimal hull form is obtained.The results of this study have a certain reference value for the initial stage of multiobjective optimization design of hull form.
文摘Optimization of cylindrical roller bearings(CRBs)has been performed using a robust design.It ensures that the changes in the objective function,even in the case of variations in design variables during manufacturing,have a minimum possible value and do not exceed the upper limit of a desired range of percentage variation.Also,it checks the feasibility of design outcome in presence of manufacturing tolerances in design variables.For any rolling element bearing,a long life indicates a satisfactory performance.In the present study,the dynamic load carrying capacity C,which relates to fatigue life,has been optimized using the robust design.In roller bearings,boundary dimensions(i.e.,bearing outer diameter,bore diameter and width)are standard.Hence,the performance is mainly affected by the internal dimensions and not the bearing boundary dimensions mentioned formerly.In spite of this,besides internal dimensions and their tolerances,the tolerances in boundary dimensions have also been taken into consideration for the robust optimization.The problem has been solved with the elitist non-dominating sorting genetic algorithm(NSGA-II).Finally,for the visualization and to ensure manufacturability of CRB using obtained values,radial dimensions drawing of one of the optimized CRB has been made.To check the robustness of obtained design after optimization,a sensitivity analysis has also been carried out to find out how much the variation in the objective function will be in case of variation in optimized value of design variables.Optimized bearings have been found to have improved life as compared with standard ones.
基金supported by the National Natural Science Foundation of China(Grant 51975298)the Natural Science Foundation of Jiangsu Province(Grant BK20181301)the National Science Foundation of China(Grant 11874303).
文摘The structure parameters of 6-degree of freedom(DOF)vibration isolation platform have a significant effect on its performance.To make the designed vibration isolation platform perform well,non-dominanted sorting genetic algorithm version II(NSGA-II)was applied to optimize its structure based on the transfer matrix method for multibody systems.Firstly,the Jacobian matrix of 6-DOF vibration isolation platform was solved based on kinematics.Secondly,the transfer equation of 6-DOF vibration isolation system was established by the linear transfer matrix method for multibody systems.And the formula of its natural frequency was derived according to the boundary conditions of the system.Thirdly,the manipulability index was constructed based on a dimensionless Jacobian matrix.And a new performance index function was established considering the influence of dynamic isotropic and legs mass.Fourthly,genetic algorithm(GA)and NSGA-II were used to optimize the structure of the 6-DOF vibration isolation platform under the same conditions,respectively.It showed that NSGA-II had higher optimization efficiency,better calculation accuracy and shorter optimization time than that of GA.Finally,NSGA-II was adopted for multi-objective optimization design of 6-DOF vibration isolation platform based on the constraint conditions.Optimal Pareto solutions were obtained,which provides structural parameters for subsequent design work.Therefore,the proposed optimization method and the performance index in this paper provide a theoretical basis for the optimal design of relevant vibration isolation mechanism.
文摘The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated.Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.
文摘This paper presents a novel general method for computing optimal motions of an industrial robot manipulator （AdeptOne XL robot） in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm （NSGA-II） and multi-objective differential evolution （MODE）, have been used for the optimization. Two methods （normalized weighting objective functions and average fitness factor） are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.
文摘Abstract A transonic, high Reynolds number natural laminar flow airfoil is designed and studied. The γ-θ transition model is combined with the shear stress transport （SST） k-w turbulence model to predict the transition region for a laminar-turbulent boundary layer. The non-uniform free-form deformation （NFFD） method based on the non-uniform rational B-spline （NURBS） basis function is introduced to the airfoil parameterization. The non-dominated sorting genetic algorithm-II （NSGA-II） is used as the search algo- rithm, and the surrogate model based on the Kriging models is introduced to improve the efficiency of the optimization system. The optimization system is set up based on the above technologies, and the robust design about the uncertainty of the Mach number is carried out for NASA0412 airfoil. The optimized airfoil is analyzed and compared with the original airfoil. The results show that natural laminar flow can be achieved on a supercritical airfoil to improve the aerodynamic characteristic of airfoils.
基金the State Key Laboratory of Compressor Technology Open Fund Project(No.SKL-YSJ201808)the National Key Research and Development Plan(No.2016YFF0203305)the Special Fund Support for Basic Scientific Research Business Expenses of Central Universities(No.JD1912).
文摘Actuator and hydraulic system parameters have great influence on the performance,safety and reliability of the stepless capacity control system of reciprocating compressor.Due to the diversity and complex relationship of parameters,traditional parameters selected and calculated based on feasibility can’t make the system run efficiently,have limitations,and may have adverse effects on the system or compressor.Therefore,taking the spring stiffness of the actuator and the impact velocity of ejection,the inlet oil pressure of the hydraulic system,and the indicated power deviation of the compressor as objective functions,the multi-parameter and multi-objective optimization research of the actuator and hydraulic system with the stepless capacity control system based on non-dominated sorting genetic algorithm II(NSGA-II)is carried out.Based on fuzzy analytic hierarchy process(FAHP),the optimal solution is selected from the Pareto front,and compared with the traditional design value,the result is better than that obtained by the traditional design method.
基金Project was supported by National Natural Science Foundation of China(Grant No.62173170).
文摘The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.
文摘Based on a thing that it is difficult to choose the parameters of active disturbance rejection control for the non-linear ALSTOM gasifier, multi-objective optimization algorithm is applied in the choose of parameters. Simulation results show that performance tests in load change and coal quality change achieve better dynamic responses and larger scales of rejecting coal quality disturbances. The study provides an alternative to choose parameters for other control schemes of the ALSTOM gasifier.
文摘In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.
基金This work was supported by the Key Program of Social Science Planning Foundation of Liaoning Province under Grant L21AGL017.
文摘Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.
基金This work is supported by National Natural Science Foundation of China(No.51277015).
文摘Demand response(DR)and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems.However,the uncertain wind power generation(WG)which has anti-peaking characteristic would be hard to exert its ability in carbon reduction.This paper introduces DR into traditional unit commitment(UC)strategy and proposes a multi-objective day-ahead optimal scheduling model for wind farm integrated power systems,since incentive-based DR can accommodate excess wind power and can be used as a source of system spinning reserve to alleviate generation side reserve pressure during both peak and valley load periods.Firstly,net load curve is obtained by forecasting load and wind power output.Then,considering the behavior of DR,a day-ahead optimal dispatching scheme is proposed with objectives of minimum generating cost and carbon emission.Non-dominated sorting genetic algorithm-II(NSGA-II)and satisfaction-maximizing method are adopted to solve the multi-objective model with Pareto fronts and eclectic decision obtained.Finally,a case study is carried out to demonstrate that the approach can achieve economic and environmental aims and DR can help to accommodate the wind power.
基金Project supported by the National Natural Science Foundation of China(No.61501399)the SAIC MOTOR(No.1925)the National Key R&D Program of China(No.2018AAA0102302)。
文摘In an unmanned aerial vehicle ad-hoc network(UANET),sparse and rapidly mobile unmanned aerial vehicles(UAVs)/nodes can dynamically change the UANET topology.This may lead to UANET service performance issues.In this study,for planning rapidly changing UAV swarms,we propose a dynamic value iteration network(DVIN)model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function,which enables UAVs/nodes to adapt to novel physical locations.We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method.Simulation results demonstrate that the proposed model significantly reduces the decisionmaking time for UAV/node path planning with a high average success rate.
基金supported by the Science and Technology Project of State Grid Corporation of China(No.52467K150007)
文摘Based on analysis of construction and operation of micro integrated energy systems(MIES), this paper presents economic optimization for their configuration and sizing. After presenting typical models for MIES, a residential community MIES is developed by analyzing residential direct energy consumption within a general design procedure. Integrating with available current technologies and local resources, the systematic design considers a prime mover, fed by natural gas, with wind power, photovoltaic generation, and two storage devices serving thermal energy and power to satisfy cooling, heating and electricity demands. Control strategies for MIES also arepresented in this study. Multi-objective formulas are obtained by analyzing annual cost and dumped renewable energy to achieve optimal coordination of energy supply and demand. According to historical load data and the probability distribution of distributed generation output,clustering methods based on K-means and discretization methods are employed to obtain typical scenarios representative of uncertainties. The modified non-dominated sorting genetic algorithm is applied to find the Pareto frontier of the constructed multi-objective formulas. In addition, aiming to explore the Pareto frontier, the dumped energy cost ratio is defined to check the energy balance in different MIES designs and provide decision support for the investors. Finally, simulations and comparision show the appropriateness of the developed model and the applicability of the adopted optimization algorithm.
