摘要
针对蚁群算法收敛速度慢的问题,对蚁群算法信息素更新规则进行研究,提出一个基于迭代思想的信息素更新规则。对信息残留因子进行实验,确定在新的信息素更新规则下信息素挥发系数的最佳合理值。最后针对eil51问题和dantzig42问题两个例子的仿真实验对比基本蚁群算法。实验结果表明,改进的蚁群算法在收敛性和求得最优解方面都明显优于基本蚁群算法和其它人工智能算法。
In order to solve the slow convergence speed problem of the ant colony algorithm, we study ant colony algorithm pheromone updating rules and propose a pheromone updating rule based on the thought of iteration. We identify the best reasonable value of the pheromone volatilization coefficient under the new pheromone updating rules through information residual factor experiments. Finally, experimental results on the two examples of eil51 and dantzig42 problems show that the improved ant colony algorithm outperforms the traditional ant colony algorithm and other artificial intelligence algorithms in terms of optimal solution and convergence.
出处
《计算机工程与科学》
CSCD
北大核心
2017年第8期1576-1580,共5页
Computer Engineering & Science
基金
广西高等学校科技研究重点资助项目(SK13ZD016)
广西研究生科研创新项目(YCSW2015155
YCSW2012066)
关键词
TSP问题
蚁群算法
信息素
TSP problem
ant colony algorithm
pheromone
