基于拥挤度因子的动态信息素更新策略蚁群算法

针对蚁群算法易陷入局部最优、收敛速度慢的问题,文中提出了一种基于拥挤度因子的动态信息素更新策略的蚁群算法(CFACS)。引入鱼群算法中拥挤度的思想,扩大种群中蚂蚁分布范围,使其探索更大的解空间,提高算法全局搜索能力;采用动态信息素更新策略,在每一次迭代中,自适应调整当前最优路径所释放的信息素浓度,保证蚁群前期的多样性,同时保证算法在后期的收敛性。求解TSP问题的仿真实验表明,改进算法求得解的质量和求解的收敛速度都明显优于传统蚁群算法,较好地平衡了种群多样性与收敛速度之间的矛盾。

Ant Colony Optimization for Dynamic Pheromone Update Strategy Based on Congestion Factor

Aiming at the problem that the ant colony algorithm is easy to fall into local optimum and the convergence speed is slow, this paper proposed an ant colony optimization based on CFACS. The idea of crowding degree in the Fish Swarm algorithm was introduced to expand the distribution range of ants in the population, which made the ants explored more solution space and improve the global search ability of the algorithm. The dynamic pheromone update strategy was adopted to adaptively adjust the pheromone concentration released by the current optimal path in each iteration to ensure the diversity of the ant colony in the early stage and to ensure the convergence of the algorithm in the later stage. The simulation experiments for solving the TSP problem showed that the improved algorithm could obtain the solution quality and the convergence speed of the solution were better than the traditional ant colony algorithm, which balanced the contradiction between population diversity and convergence speed.