求解TSP问题的自适应邻域遗传算法

Adaptive neighborhood method & Genetic Algorithm for solving Travelling Salesman Problem

提出结合自适应邻域法与遗传算法来求解TSP问题。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时,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。仿真实验结果表明所提算法与其他算法相比具有竞争能力。

In this paper, the travelling salesman problem（TSP） is solved by using the adaptive neighborhood method ＆ genetic algorithm.In adaptive neighborhood method（ANM） one mimics the traveller whose rule shows that the next city is not always the nearest as-yet-unvisited location but it＇s randomly selected from the unvisited cities which are further than the nearest city 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 keeps 90% samples from the previous generation;the remaining is supplied by the new samples created by ANM.The results of simulation show that this approach is more competitive than other algorithms.