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中文摘要：
蚂蚁殖民地优化(ACO ) 算法被修改优化全球路径。为了模仿真实蚂蚁殖民地，根据蚂蚁殖民地和食物的特征的 foraging 行为，附近的区域和气味区域的概念被介绍。Theformer 能保证路径的差异，后者保证每只蚂蚁能到达目标。然后，整个路径被划分成三部分， ACO 被用来寻找第二条部分路径。三部分什么时候轻拍 hes，被调整，最后的路径被发现。有效路径和无效路径被定义保证路径有效。最后， pheromone 搜索的策略被使用寻找最佳路径。然而，当仅仅 pheromone 被用来寻找最佳路径时， ACO 容易收敛。为了避免这早熟的集中，联合 pheromone 搜索和随机，寻找，一个混合蚂蚁殖民地算法(HACO ) 被用来发现最佳路径。在 ACO 和 HACO 之间的比较证明 HACO 能被用来发现最短的路径。
英文摘要：
Ant colony optimization （ACO） algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted, the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm（HACO） was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.
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