一种基于惩罚函数和新信息素更新方式的蚁群算法

An ant colony algorithm based on penalty function and new pheromone updating

提出一种快速求解旅行商问题的蚁群算法。首先给出了一种新的信息素搜索模型,降低了搜索过程的复杂性,提高了路径搜索的准确性。其次通过设置惩罚函数,排除不相关路径,减小搜索范围。实验结果表明,该算法能较好地得到最优解,提高收敛速度。

An fast ant colony algorithm solving traveling salesman problem is presented. Firstly, this paper gives a new pheromone updating model. It reduces the complexity of the search process, improves the accuracy of the route searching. Secondly, by setting penalty function, to exclude irrelevant path so that the narrow scope of the search process. Experiments show that, this algorithm can get better opti- mal solution, and improve the convergence speed significantly.