Abstract:

An improved ant colony algorithm is proposed to address issues such as susceptibility to local optima and slow convergence speed. Firstly, the relationship between the current target node and the next target node and the normal distribution function are introduced into the heuristic function, enhancing the algorithm's search capability in the early stages. In addition, by introducing an inflection point factor, the diversity of directional selection is enhanced. Secondly, an adaptive dynamic pheromone volatility coefficient is proposed to adjust the pheromone evaporation rate adaptively, modifying the pheromone update rules. Finally, simulation experiments were conducted using Matlab to compare the traditional ant colony algorithm and the improved ant colony algorithm on three different grid maps. The experimental results demonstrate that, compared with the traditional ant colony algorithm, the improved algorithm exhibits advantages such as faster convergence speed, shorter paths, and fewer inflection points.

Key words: Ant colony, Heuristic function, Normal distribution function, Inflection point factor, Path planning

摘要：

针对容易陷入局部最优解、收敛速度慢等问题的传统蚁群算法，提出了一种改进蚁群算法。首先，将当前目标节点与下一时刻要选择的节点之间的关系以及正态分布函数引入启发函数中，增强了算法在前期的搜索能力，并通过引入拐点因子加强了方向选择的多样性；其次，提出自适应动态信息素挥发系数，改变了信息素更新规则；最后，通过Matlab仿真实验，在三种不同栅格图上对传统蚁群算法和改进蚁群算法进行了对比研究。实验结果证明，与传统蚁群算法相比，改进蚁群算法具有收敛速度快、路径短、拐点少等优点。

关键词: 蚁群算法, 启发函数, 正态分布函数, 拐点因子, 路径规划