基于信息素更新和挥发因子调整改进蚁群算法
张永强王晓东
(西安工程大学理学院,陕西西安 710048)
摘要:基本蚁群算法存在易陷入局部最优解、收敛速度慢等缺点,本文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,,改进蚁群算法比基本蚁群算法(15602)得到更优路径长度为15483.
关键词:蚁群算法;信息素;TSP
中图分类号:TP312 文献标志码:A
Improved ant colony optimization algorithm based on
pheromone updating and evaporation
factor adjusting
ZHANG Yong-qiang , WANG Xiao-dong
(School of Science, Xi’an Polytechnic University, Xi’an 710048, China)
Abstract: The basic ant colony algorithm converges slowly, is prone to plunge into partial optimum and results in search stagnation. In this paper, an improved ant colony algorithm is proposed. New algorithm introduces positive and negative feedback regulation of pheromone increment size, and the pheromone evaporation factor randomized to adjust the amount of pheromone on the path. The simulation results of traveling salesman problem show that improved algorithm has been better path length is 15438 than the basic ant colony algorithm(15602).
Key words: ant colony algorithm; pheromone; TSP
针对蚁群算法易陷于局部最优解,搜索时间长等缺点,,限制了残留信息量,德国学者Thomas sttzle与Jolger Hoos提出了最大最小蚁群系统算法[1],将各条路径上的信息素浓度限制在一定的范围内,避免某条路径的信息量远大于其他路径,、易限于局部最优解等缺陷,刘瑞杰,胡小兵[2]提出基于动态调节信息素增量的蚁群算法;孟祥萍,片兆宇,沈中玉等[3]提出了基于方向信息素协调的蚁群算法;张家善,王志宏[4]引入信息素调节系数,提出了基于信息素的改进蚁群算法及其在TSP中的应用;郑卫国,田其冲,张磊[5]对蚂蚁进行区分,控制信息素浓度,提出了基于信息素强度的改进蚁群算法;侯文静,马永杰等[6]提出了一
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