求解空走优化路径的自适应邻域遗传算法

Adaptive neighborhood method & GA for solving the vacancy route optimization of machining

建立激光加工中空走路径优化的数学模型,并转化为旅行商问题(TSP)来求解。对最近邻法进行改进形成自适应邻域法。在自适应邻域法中,从某个城市出发,下一城市不一定是其最近城市,而是在比其最近城市稍远的邻域范围进行动态随机选取。在求解TSP的遗传算法中,采用自适应邻域法对种群初始化,然后采用选择、交叉、变异进行迭代,在选择中仅保留父代90%的样本,剩下的采用自适应邻域法产生新样本进行补充。运行结果表明,该算法缩短了激光加工空行程,提高了加工效率。

The mathematical model of vacancy course path optimization of laser machining is built and changed to the travelling salesman problem （TSP）. The Nearest Neighbor （NN） is modified to Adaptive Neighborhood Method （ANM）. In ANM one mimics the traveller whose rule of thumb is not always to go next to the nearest as-yet-unvisited location. The next city is randomly selected from the unvisited cities in adaptive neighborhood. While solving the TSP,ANM is used to create the initial population at first,then iterations are done through selection,cross and mutation operation. In selection,the proposed algorithm only keep 90% samples from the previous generation,the remained agents are supplied by the new sample created by ANM. The results show that the algorithm shortens vacancy course in laser machining and the manufacturing efficiency is improved.