The heuristic information is the key in the geographic information system(GIS). Ant Colony Algorithm(ACO) easily leads to local optimal solution in solving shortest path problem. To overcome this shortcoming, improved ACO with heuristic information is proposed. The ACO introduces heuristic information to guide ants can fast converge to global optimal solution in the initialization of the ant colony. And, the ACO uses dual population search and improves state transition operator for the balance of global and local search capabilities to effectively improve the performance and increase the diversity. There use C++ programming of Visual Studio 2005. net. The results show that this algorithm can not only effectively solve the shortest path problem of GIS, but also has high convergence precision.%启发信息是地理信息系统(GIS)中的关键,针对蚁群算法易陷入局部最优的缺陷,提出一种带有启发信息的改进蚁群算法.该算法在初始化蚁群时引入启发信息指引蚂蚁快速收敛于全局最优解,为平衡全局与局部搜索能力,也改进状态转移概率算子,从而有效提高算法性能,增加种群多样性.实验以Visual Studi02005中C++编程实现仿真,结果表明此算法不但能有效求解GIS的最短路径,而且改进的算法能快速地收敛且精度高.
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