基于信息素更新和挥发因子调整改进蚁群算法

摘要

针对基本蚁群算法存在易陷入局部最优解、收敛速度慢等缺点,运用正负反馈调节信息素增量大小,并将信息素挥发因子随机化,使得蚁群算法自动调整路径上的信息素量。将改进后的蚁群算法运用到31个城市的旅行商线路问题优化中,结果表明,基本蚁群算法路径长度为15602,改进蚁群算法路径长度为15483,改进蚁群算法优于基本蚁群算法。%Aiming at the problem that the basic ant colony algorithm converges is prone to plunge into partial optimum and result in search stagnation.An improved ant colony algorithm is proposed.The algorithm introduces positive and negative feedback regulation of pheromone increment size,and the pheromone evaporation factor is randomized to adjust the amount of pheromone on the path.The improved ant colony algorithm is used in TSP(travelling salesman problem)optimization in 31 cities.The results show that the improved algorithm has better path length which is 15 438 than the basic ant colony algorithm whose path length is 15 602.