Abstract: An improved ant colony algorithm is proposed for the optimization of continuous problems.In the algorithm a method of auto-adapt search space is presented to tighten the range of search space gradually.Pheromone diffusion mechanism is adopted to enhance the search near the optimal solution.With these methods the ant colony algorithm has improved convergance speed and solution accuracy.During the simulation of trajectory optimization，the range of search space was evidently reduced and the optimal trajectory was obtained.The result proves the validity of the algorithm.

Key words: trajectory optimization, ant colony algorithm, pheromone

摘要： 针对连续空间的优化问题提出了一种改进蚁群算法及搜索空间的自适应调整方法，将搜索空间逐步缩小到最优解附近，并通过信息素扩散机制增强对最优解附近区域的搜索，这些改进措施有利于改善蚁群算法的收敛速度和提高算法的求解精度。将这种改进算法应用到弹道优化过程中，可以有效收缩搜索空间范围获得高精度的最优弹道，这说明了算法的有效性。

关键词: 弹道优化, 蚁群算法, 信息素