基于遗传蚁群混合算法的孔群加工路径优化  

Holes Machining Path Optimization Based on a Hybrid Algorithm Integrated Genetic Algorithm with Ant Colony Optimization

作　　者：王春香[1] 郭晓妮[1] 

机构地区：[1]内蒙古科技大学机械工程学院,内蒙古包头014010

出　　处：《机床与液压》2011年第21期43-45,4,共4页Machine Tool & Hydraulics

基　　金：内蒙古自然科学基金项目(200711020713);包头市科技发展基金资助项目(2008)

摘　　要：为了提高孔群的数控加工效率,以孔群加工路径最短为目标函数,采用遗传蚁群混合算法对孔群加工路径规划问题进行研究。该混合优化算法的前期采用遗传算法、后期采用蚁群算法。在遗传算法向蚁群算法转换过程中,提出一种GSA遗传解到信息素转化策略。该策略以在遗传解endpop中选取前90%个个体和再随机产生的10%个个体合并后组成的新矩阵作为信息素值的转化依据;同时探讨了遗传算法中遗传算子的最佳组合问题。实例计算结果表明:与传统分批按编号加工的路径相比较,采用最佳组合算子和GSA转化策略后的遗传蚁群混合算法求解问题所获得的孔群加工路径缩短了70.9%,比单一遗传算法具有更高的求解精度,理论上可以明显地提高孔群的数控加工效率。In order to improve the efficiency of holes NC machining, with the shortest path to holes machining as the objective function, a hybrid algorithm （HA） integrated genetic algorithm （GA） with ant colony optimization （ACO） for solving holes machining path optimization was studied. The GA was run first and then ACO in the hybrid algorithm. A new strategy called GSA was proposed aiming at the key link in the ＂HA＂ that converted genetic solution from GA into information pheromone to distribute in ACO. The new matrix was taken by the GSA, which was formed by the combination of the former 90% of individual from genetic solution and 10% of individual by random generation as the basis of transformation of pheromone value. The best combination of genetic operators in GA was also discussed. The experimental results show that with the traditional processing route by numbers compared, by the HA using optimal combination operator and GSA transformation strategy, the length can be shortened for 70. 9%, and has higher precision than a single genetic algorithm. The NC machining efficiency of holes can be obviously improved theoretically.

关 键 词：孔群加工路径优化 遗传算法 蚁群算法 混合算法 

分 类 号：TH16[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]