中文摘要：

为解决蚁群算法存在的收敛速度慢和容易陷入局部最优等缺点,分析了其产生的主要原因,介绍了AS和MMAS算法的工作原理,并基于参数自适应思想,提出了全局自适应蚁群优化算法（GAO）.对状态转移和信息素更新等规则做出改进,详尽给出了GAO的编程步骤.针对TSP问题,通过与AS和MMAS算法的数值实验结果比较分析,表明GAO算法具有优良的全局优化能力和适当的收敛时间.

英文摘要：

Ant colony algorithm has slow convergence speed and is easy to fall in local optimal, in order to overcome these shortcomings, the principles of AS algorithm and MMAS algorithm were introduced. Then the main reasons of these shortcomings were analyzed and based on the idea of parametric adaptation an improved ant colony algorithm with global adaptive optimization （GAO） was put forward. Some improvements of rules were made such as state transfer and pheromone update, and then the detailed program steps of GAO were listed. Compared with AS and MMAS for the Traveling Salesman Problem （TSP）, the simulation results show that GAO has excellent global optimization capabilities and appropriate convergence time.