The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa...The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.展开更多
Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present...Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.展开更多
An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector w...An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.展开更多
Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating...Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it,the results of this model are still not sufficiently accurate.In this study,in order to increase the efficiency of SRC model,a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) algorithm.The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River,Iran are used as a case study.In the first part of the study,using self-organizing map (SOM),an unsupervised artificial neural network,the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models,respectively.In the second part of the study,two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was evaluated.The comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-Ⅱ algorithm was superior to the SRC models in the daily SSL estimation for the data used in this study.Given that the use of the SRC model is common,the proposed model in this study can increase the efficiency of this regression model.展开更多
针对超导电机多层绝热(multi-layer insulation,MLI)材料低热流密度需求与紧凑型整机要求的设计矛盾,提出一种基于改进逐层(layer by layer,LBL)模型与二代非支配排序遗传算法的多层绝热多目标协同优化方法。首先,基于辐射传热、气体导...针对超导电机多层绝热(multi-layer insulation,MLI)材料低热流密度需求与紧凑型整机要求的设计矛盾,提出一种基于改进逐层(layer by layer,LBL)模型与二代非支配排序遗传算法的多层绝热多目标协同优化方法。首先,基于辐射传热、气体导热与固体导热方程,通过引入间隔层光学性质、反射屏开孔率及动态自适应系数等关键参数,提升常规LBL模型的计算精度。其次,结合各项传热占比特点,构建最大为四密度区的变密度MLI模型。然后,采用非支配排序遗传算法,以各密度区层数为优化变量,以改进LBL模型作为算法适应度计算函数,约束各密度区层数以及各密度区层数之和,经种群进化得到Pareto前沿。在此基础上,进一步分析了绝热材料热流密度与密度区数量、各密度区层数、层密度的关系以及变密度敷设对热流分布的调控能力。研究结果表明,优化方案中热流密度覆盖0.42~3.11 W/m^(2)、厚度覆盖5.5~43.0 mm,敷设方式覆盖不同层密度的定密度方案、两密度区变密度、三密度区变密度以及四密度区变密度方案。通过调控密度区数量以及密度区的层数、层密度可以实现多层绝热材料优化,降低后续施工难度。展开更多
The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response su...The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. Non- dominated sorting genetic algorithm-II is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.展开更多
The second Egyptian research reactor ET-RR-2 went critical on the 27th of November 1997.The National Center of Nuclear Safety and Radiation Control (NCNSRC) has the responsibility of the evaluation and assessment of t...The second Egyptian research reactor ET-RR-2 went critical on the 27th of November 1997.The National Center of Nuclear Safety and Radiation Control (NCNSRC) has the responsibility of the evaluation and assessment of the safety of this reactor.The purpose of this paper is to present an approach to optimization of the fuel element plate. For an efficient search through the solution space we use a multi objective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets.The aim of this paper is to propose a new approach for optimizing the fuel element plate in the reactor.The fuel element plate is designed with a view to improve reliability and lifetime and it is one of the most important elements during the shut down.In this present paper,we present a conceptual design approach for fuel element plate,in conjunction with a genetic algorithm to obtain a fuel plate that maximizes a fitness value to optimize the safety design of the fuel plate.展开更多
Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optic...Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.展开更多
In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location...In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.展开更多
基金Project supported by the National Basic Research Program of China (973 Program) (No. 2007CB714600)
文摘The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.
文摘Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.
基金supported by the National Natural Science Foundation of China (60632050)National Basic Research Program of Jiangsu Province University (08KJB520003)
文摘An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.
文摘Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it,the results of this model are still not sufficiently accurate.In this study,in order to increase the efficiency of SRC model,a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) algorithm.The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River,Iran are used as a case study.In the first part of the study,using self-organizing map (SOM),an unsupervised artificial neural network,the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models,respectively.In the second part of the study,two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was evaluated.The comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-Ⅱ algorithm was superior to the SRC models in the daily SSL estimation for the data used in this study.Given that the use of the SRC model is common,the proposed model in this study can increase the efficiency of this regression model.
文摘The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. Non- dominated sorting genetic algorithm-II is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.
文摘The second Egyptian research reactor ET-RR-2 went critical on the 27th of November 1997.The National Center of Nuclear Safety and Radiation Control (NCNSRC) has the responsibility of the evaluation and assessment of the safety of this reactor.The purpose of this paper is to present an approach to optimization of the fuel element plate. For an efficient search through the solution space we use a multi objective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets.The aim of this paper is to propose a new approach for optimizing the fuel element plate in the reactor.The fuel element plate is designed with a view to improve reliability and lifetime and it is one of the most important elements during the shut down.In this present paper,we present a conceptual design approach for fuel element plate,in conjunction with a genetic algorithm to obtain a fuel plate that maximizes a fitness value to optimize the safety design of the fuel plate.
文摘Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.
基金Natural Science Foundation of Shanghai,China(No.15ZR1401600)the Fundamental Research Funds for the Central Universities,China(No.CUSF-DH-D-2015096)
文摘In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.
