具有路径平滑和信息动态更新的蚁群算法

Ant Colony Optimization Algorithm with Path Smoothing and Dynamic Pheromone Updating

蚁群算法具有很强的寻优能力,但仍存在搜索时间过长、易于停滞等问题。针对这些不足,提出了一种具有路径平滑和信息动态更新的蚁群算法。新算法引入了路径平滑概念,加强了对蚁群前期搜索的引导,扩大了蚁群后期搜索空间;同时,通过动态调节信息素挥发因子,使得路径间信息素浓度差异不会增长过快,有效地避免了算法陷入局部解。实验结果表明,具有路径平滑和信息动态更新的蚁群算法明显优于基本蚁群算法。

Ant colony optimization is a new heuristic algorithm which has been proven a successful technique for combinatorial optimization problems,but it still has some shortcomings such as stagnation behavior, needing much time and premature convergence. A new algorithm based on path smoothing and dynamic pheromone updating was proposed for overcoming those shortcomings. By path smoothing, in the early convergence phase, ants will search towards the path with shorter distance;ants will more constructe pheromone in the later convergence phase. By dynamic pheromone updating,algorithm can avoid being trapped into local optimum. The experimental results show that the algorithm presented in this paper has more effective than classical ant colony algorithm.