基于HCGA的三坐标测量路径优化

Path Planning Method Based on HCGA in Coordinate Measuring Machine

针对传统遗传算法（GA）进行三坐标钡0量路径优化时收敛速度慢且过早收敛的问题，提出基于爬山遗传算法（HCGA）的三坐标测量路径优化方法。根据三坐标测量路径优化的数学模型，构造适合于测量路径优化问题的遗传编码、初始种群、选择、交叉、变异等参数，通过增加爬山操作，加快GA的迭代收敛速度。仿真实验结果表明，基于HCGA的三坐标测量路径优化方法能有效提高局部寻优能力与收敛速度，可获得很好的最优解并提高了测量效率。

Aiming at the problem of slow convergence speed and premature convergence in using the genetic algorithm （GA） to conduct the measuring path optimization in coordinate measuring machine （CMM）, the path optimization algorithm in coordinate measuring machine based on hill-climbing genetic algorithm （HCGA） was proposed. According to characteristics of the path optimization in coordinate measuring machine, the mathematic model of the measuring path optimization was built and the parameters of coding, initial group, selection, cross for measuring path optimization problems were constructed. In addition, the climbing was added to increase convergence speed of the GA for the global optimal solution. Besides, the simulation was programmed to verify the effectiveness of the HCGA and the application examples were pointed out. The resuhs show that when using the HCGA to conduct the path optimization algorithm in coordinate measuring machine, the local optimization ability of GA and the convergence speed were improved and the very good optimal solution was obtained. Therefore, the measuring efficiency was improved.