Optimization of helical gear volume based on Python and adaptive genetic algorithm
To optimize the volume of the helical gear in the helical swing cylinder.The range and minimum value of modular tooth number in traditional design were expressed through program language to form constraints,and then the randomly generated population was formed by adaptive selection,crossover,mutation and other operations.The elite strategy was used to select excellent individuals to generate new populations,and the operations were repeated until the required number of iterations was completed.Finally,the optimal individual for each generation is printed.Both traditional algorithm and adaptive genetic algorithm can optimize the volume of helical gear,and finally reduce the volume of helical gear by 5.87％.Adaptive genetic algorithm can optimize the volume of helical gear,and has obvious advantages in iteration speed.The multiple solutions generated in the process can guide the actual design,reduce the calculation amount and design time during design,and reduce the production cost.
万方数据
维普
