􀁽 蚁群算法是通过模拟蚂蚁觅食而发展出的一种新的启发算法􀁱 基于群体的协作与学习􀁯 该算法已

经成功地解决诸如× ≥° 问题等多种组合优化问题􀁱 本文提出了一种基于自适应调整信息素的改进蚁群算法􀁱

该算法根据人工蚂蚁所获得解的情况􀁯 动态地调整路径上的信息素􀁯 从而使得算法跳离局部最优解􀁱 通过仿真

实验获得的结果表明􀁯 该算法对于蚁群算法具有较好的改进效果-􀁽　Ant colony algorithm is developed through the simulation of ant foraging out of a new heuristic algorithm 􀁱　population-based collaboration and learning 􀁯　that the algorithm has been successfully resolved the problem, such as × ≥ ° 􀁱　a variety of combinatorial optimization problems proposed in this paper a kinds of adaptive pheromone-based ant colony algorithm to improve the algorithm based on artificial ants 􀁱　obtained by solution of 􀁯　dynamically adjust the pheromone on the path which makes the algorithm 􀁯　jumped off the local optimal solution 􀁱　results obtained through simulation experiments show that 􀁯　The algorithm for improved ant colony algorithm has good effect