Through mechanism analysis of simple geneticalgorithm (SGA), every genetic operator can be considered as a linear transform. So some disadvantages of SGA may be solved if genetic operators are modified to nonlinear t...
详细信息
ISBN:
(纸本)0780378652
Through mechanism analysis of simple geneticalgorithm (SGA), every genetic operator can be considered as a linear transform. So some disadvantages of SGA may be solved if genetic operators are modified to nonlinear transform. According to the above method, the nonlinear genetic algorithm is introduced, and different nonlineargenetic operators with some probability are designed and applied to numerical optimization problems. The optimization computing of some examples is made to show that the new geneticalgorithm is useful and simple.
By the expansion of Hamming distance, a new geometry model-distance space is introduced. In this space, mechanism analysis of crossover and mutation operators is made. According the analysis, a new mu
By the expansion of Hamming distance, a new geometry model-distance space is introduced. In this space, mechanism analysis of crossover and mutation operators is made. According the analysis, a new mu
Model-based techniques can accurately locate the vicinity of leak localization. However, in the traditional hydraulic leakage model (THLM), the nodal flow is composed of the actual water consumption and the background...
详细信息
Model-based techniques can accurately locate the vicinity of leak localization. However, in the traditional hydraulic leakage model (THLM), the nodal flow is composed of the actual water consumption and the background leakage calculated in equal proportion, without considering the influence of pressure on the background leakage. Therefore, in this research, the parameter alpha is obtained through a pipe network experiment. According to the relationship between the background leakage and the pressure, the parameter beta is calculated with a nonlinear genetic algorithm (NGA). The emission coefficient C is obtained based on the length of the pipeline, and then the pressure-driven background leakage model (PDBLM) is built to detect the leak localization using a multi-population geneticalgorithm (MPGA). Through the simulation results under three working conditions, it is concluded that the PDBLM model is closer to the operation of the actual pipe network than the THLM model. Additionally, the PDBLM-based Inverse Problem Leak Localization Model can find the actual leakage point more accurately, improve leakage detection efficiency, and reduce water loss.
This paper puts forward a geneticalgorithm based on non-linear programming in order to deal with the inverse kinematics solution precision of robot arm for watermelon picking, ensure the yield after picking and impro...
详细信息
ISBN:
(纸本)9781509064151;9781509064144
This paper puts forward a geneticalgorithm based on non-linear programming in order to deal with the inverse kinematics solution precision of robot arm for watermelon picking, ensure the yield after picking and improve the fruit quality after picking. The robot arm for watermelon picking adopts the model of Denavit-Hartenberg, which mainly applies the non-linear geneticalgorithm to give solution on the inverse kinematics issues. Lastly the paper differentiate the obtained inverse kinematics parameters through random forests algorithm. This paper respectively applies the geneticalgorithm and nonlinear programming geneticalgorithm for inverse kinematics solution on robot arm for watermelon picking with five degrees, six degrees and seven degrees of freedom. The experiment result shows that the non-linear programming geneticalgorithm could effectively give inverse kinematics solution on robot arm for watermelon picking with multiple degrees of freedom, with the solution precision 300 to 600 times that of the geneticalgorithm. There are more than one solution for the inversion result of the robot arm and the random forests algorithm could select fairly good picking path and gesture to reduce the unnecessary damage to the watermelon fruits in the picking.
暂无评论