基于改进遗传算法的多类图元混合加工路径优化方法

Hybrid Machining Path Optimization Method for Multi-Class Graphic Elements Based on Improved Genetic Algorithm

为解决数控加工中复杂轨迹的排序规划问题,提出基于改进遗传算法的多类图元混合加工路径优化方法。针对不同轨迹段图形进行分类编码设计,将适用多类图元混合路径优化的第二类GTSP模型转化为TSP问题,同时在遗传进化过程中采用线性定标和自适应遗传算子等方式进行全局路径排序,最后通过封闭式与非封闭式轨迹段的起点计算与局部寻优求解最短路径。通过扩展应用开源GAlib库进行了测试,试验证明:算法快速收敛,有效解决多类图元混合路径优化问题,可提升数控机床加工效率。

In order to solve the problem of complex trajectory planning problem in the process of NC machining in many industries, hybrid machining path optimization method for multi-class graphic elements based on Improved Genetic Algorithm is proposed. Classification and coding design of different track segments is presented in this method, which transmuted the second category of GTSP model suitable for hybrid path optimization of multi-class graphic elements into TSP model, and in the process of genetic evolution, some methods such as linear scaling and adaptive genetic operator is used to sort the global path. Finally, through the calculation of the starting point and the local optimization of the closed and non closed trajectory segments, the shortest path is solved. By the extended application of the open source GAlib library, the improved algorithm is proved to fast convergence, and can effectively solve the problem of hybrid path optimization of multi-class graphic elements, and improve the machining efficiency of NC machine tools.