To research the effect of the selection method of multi-objects genetic algorithm problem on optimizing result, thismethod is analyzed theoretically and discussed by using an autonomous underwater vehicle(AUV) as an o...To research the effect of the selection method of multi-objects genetic algorithm problem on optimizing result, thismethod is analyzed theoretically and discussed by using an autonomous underwater vehicle(AUV) as an object. A changingweight vtlue method is put forward and a selection formula is modified. Some experiments were implemented on an AUV.TwinBurger. The results shows that this method is effective and feasible.展开更多
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
By analyzing a combined and spatial 6-bar linkage weft insertion mechanism, its practical model for optimization design is set up and the modification of penalty strategy is put forward so that the genetic algorithm c...By analyzing a combined and spatial 6-bar linkage weft insertion mechanism, its practical model for optimization design is set up and the modification of penalty strategy is put forward so that the genetic algorithm can be better used in optimization design for mechanisms with non- linear constraints. The design result is discussed.展开更多
This paper presents the method of solving the equations of motions by evolutionary algorithms. Starting from random trajectory, the solution is obtained by accepting the mutation if it leads to a better...This paper presents the method of solving the equations of motions by evolutionary algorithms. Starting from random trajectory, the solution is obtained by accepting the mutation if it leads to a better approximations of Newton’s second law. The general method is illustrated by finding trajectory to the Moon.展开更多
Two new heuristic models are developed for motion planning of point robots in known environments.The first model is a combination of an improved particle swarm optimization (PSO) algorithm used as a global planner and...Two new heuristic models are developed for motion planning of point robots in known environments.The first model is a combination of an improved particle swarm optimization (PSO) algorithm used as a global planner and the probabilistic roadmap (PRM) method acting as a local obstacle avoidance planner.For the PSO component,new improvements are proposed in initial particle generation,the weighting mechanism,and position-and velocity-updating processes.Moreover,two objective functions which aim to minimize the path length and oscillations,govern the robot’s movements towards its goal.The PSO and PRM components are further intertwined by incorporating the best PSO particles into the randomly generated PRM.The second model combines a genetic algorithm component with the PRM method.In this model,new specific selection,mutation,and crossover operators are designed to evolve the population of discrete particles located in continuous space.Thorough comparisons of the developed models with each other,and against the standard PRM method,show the advantages of the PSO method.展开更多
文摘To research the effect of the selection method of multi-objects genetic algorithm problem on optimizing result, thismethod is analyzed theoretically and discussed by using an autonomous underwater vehicle(AUV) as an object. A changingweight vtlue method is put forward and a selection formula is modified. Some experiments were implemented on an AUV.TwinBurger. The results shows that this method is effective and feasible.
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By analyzing a combined and spatial 6-bar linkage weft insertion mechanism, its practical model for optimization design is set up and the modification of penalty strategy is put forward so that the genetic algorithm can be better used in optimization design for mechanisms with non- linear constraints. The design result is discussed.
文摘This paper presents the method of solving the equations of motions by evolutionary algorithms. Starting from random trajectory, the solution is obtained by accepting the mutation if it leads to a better approximations of Newton’s second law. The general method is illustrated by finding trajectory to the Moon.
文摘Two new heuristic models are developed for motion planning of point robots in known environments.The first model is a combination of an improved particle swarm optimization (PSO) algorithm used as a global planner and the probabilistic roadmap (PRM) method acting as a local obstacle avoidance planner.For the PSO component,new improvements are proposed in initial particle generation,the weighting mechanism,and position-and velocity-updating processes.Moreover,two objective functions which aim to minimize the path length and oscillations,govern the robot’s movements towards its goal.The PSO and PRM components are further intertwined by incorporating the best PSO particles into the randomly generated PRM.The second model combines a genetic algorithm component with the PRM method.In this model,new specific selection,mutation,and crossover operators are designed to evolve the population of discrete particles located in continuous space.Thorough comparisons of the developed models with each other,and against the standard PRM method,show the advantages of the PSO method.
