一种引入信息素上下界自适应机制的蚁群算法

使用传统蚁群算法求解最优路径问题时，存在搜索速度慢且易于陷入局部最优解等缺陷．针对这个问题，提出一种改进的蚁群算法：在每次迭代结束后，根据本次迭代产生的最优解与当前最优解的比较结果，动态调整路径上信息素的上下界，使路径上信息素永远保持在一个被允许的范围内，从而避免使算法过早陷入局部最优解．仿真实验证明：改进的蚁群算法较传统的蚁群算法的搜索性能有较大的提高．

A Kind of Ant Colony Algorithm with Adaptive Strategy of Pheromone Lower and Upper Bounds

When resolving the optimal path problem with traditional ant colony algorithms, it is shown that its speed is slow and is prone to fall into local optimization. To deal with this problem, a kind of modified ant colony algorithm is proposed. After each iteration, according to the comparison result of optimization produced in this iteration and optimization, the range of adjustment to put the pheromone on a pheromone range and to avoid falling into local optimization early is by using simulated experiments to show that modified ant colony algorithms have better searching ability than a traditional one.