基于动态调节信息素增量的蚁群算法

摘要

This paper studied the updating rule of pheromone and employed a new regulatory factor in this paper to avoid the stagnation, so the pheromone on the routes close to the shortest increased, others didn't change obviously, which encouraged ants to find better solution in less time. Simulation results and convergence process show that the improved ant colony system has better performance in solving the traveling salesman problems (TSP).%为了避免蚁群算法陷入停滞状态,研究了信息素的更新规则,并在信息素增量更新式中加入动态调节因子,使得次优路径上的信息素增量较大,其他路径则没有明显的变化,从而有利于蚂蚁在较短的时间内找到更好的解.仿真实验结果及收敛过程表明,改进后的算法解决旅行商问题具有更好的全局搜索能力.