中文摘要：

旅行商问题是一个经典的数学组合优化问题,其广泛的工程应用背景促进了旅行商问题求解方法的快速发展。针对旅行商问题中最优路径的连接特点,提出了两种邻域搜索方法：邻域随机性搜索法和邻域概率性搜索法。这两种邻域搜索法对旅行商问题解的质量具有一定的提高能力,其中,为了加快搜索速度,在算法前期采用了循环倒置算子。实验结果表明算法在求解小规模旅行商问题时具有良好的寻优性能。最后将该算法与标准遗传算法结合,并进行了实验结果对比。实验数据显示结合后的算法搜索性能优于单一的两种优化算法,提高了算法搜索解的能力。

英文摘要：

Traveling salesman problem,as a classic mathematical optimization problem,with the extensive background of engineering application,promotes the development of the solution methods. For the connected characteristics of the optimal path of the traveling salesman problem,this paper puts forward two kinds of neighborhood search methods,i. e. the random search method in the neighborhood and probabilistic search method in the neighborhood,which improve the quality of the solutions. Among them,in order to speed up the search speed,loop inversion operator is used in the early process of the algorithm. The experimental results show that the algorithm has a good optimization performance when solving traveling salesman problem with small scale. Finally,the proposed algorithm is combined with the standard genetic algorithm and compared with the standard genetic algorithm. The experimental data shows that the combinational algorithm is better than the single optimization ones and improves the searching ability.