这种算法是目前国内外启发式算法中的研究热点和前沿课题，被成功地运用于旅行商问题的求解，蚁群算法在求解复杂优化问题方面具有很大的优越性和广阔的前景。但是,根据观察实验发现，蚁群中的多个蚂蚁的运动是随机的，在扩散范围较大时，在较短时间内很难找出一条较好的路径，在算法实现的过程中容易出现停滞现象和收敛速度慢现象。在这种弊端的情况下，学者们提出了一种自适应蚁群算法，通过自适应地调整运行过程中的挥发因子来改变路径中信息素浓度,从而有效地克服传统蚁群算法中容易陷入局部最优解和收敛速度慢的现象。-This algorithm is a heuristic algorithm in the current domestic and international research focus and topics at the forefront, has been successfully applied to solve the traveling salesman problem, ant colony algorithm in solving complex optimization problems has great advantages and broad prospects. However, based on observation was found in multiple ant colony movement is random, the diffusion range is large, in a relatively short time is difficult to find a better path in the algorithm implementation process prone stagnation and slow convergence phenomenon. In this malpractice case, scholars have proposed an adaptive ant colony algorithm, by adaptively adjusted during operation to change the path of volatile factor pheromone, which effectively overcome the traditional ant colony algorithm easy to fall into local optimal solution and slow convergence phenomenon.