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
Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when mode...Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.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
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.
基金supported by National Natural Science Foundation of China (No.60474059)Hi-tech Research and Development Program of China (863 Program,No.2006AA04Z160).
文摘Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.
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.
