摘要
蚁群算法是受蚂蚁觅食行为启发的智能仿生优化算法,在求解TSP这一组合优化问题时行之有效。笔者针对基本蚁群算法求解TSP时存在易于陷入局部最优解、过早停滞的缺陷,结合引入参数、分阶段迭代对基本蚁群算法做出改进,并将改进的蚁群算法与基本蚁群算法解决旅行商问题的实验结果进行对比分析,验证改进蚁群算法的效果。
Ant colony algorithm is an intelligent bionic optimization algorithm inspired by ant foraging behavior.It is effective in solving the TSP combination optimization problem.For the basic ant colony algorithm,the TSP is easy to fall into the local optimal solution and the premature stagnation.Combined with the introduction of parameters and phased iteration,the basic ant colony algorithm is improved.The improved ant colony algorithm and the experimental results of the basic ant colony algorithm for solving the traveling salesman problem are compared and analyzed,and the effect of the improved ant colony algorithm is verified.
作者
苏晓勤
Su Xiaoqin(School of Information Engineering,Tianjin University of Commerce,Tianjin 300130,China)
出处
《信息与电脑》
2019年第20期42-43,共2页
Information & Computer
关键词
蚁群算法
TSP
信息素
ant colony algorithm
TSP
pheromone
