摘要
针对传统蚁群算法在复杂网络路径规划中收敛速度慢和易陷入局部最优解的问题,本文在蚁群算法状态转移概率公式的基础上考虑了安全性因素和加权因子,在全局信息素更新过程中引入自适应动态因子,提出改进的自适应蚁群算法以更快地获取全局最优解。将改进自适应蚁群算法应用于移动机器人的路径规划,使用可视图法描绘出障碍物图像,通过真实数据进行实验分析,证明了改进自适应蚁群算法比传统蚁群算法、改进蚁群算法的收敛速度更快,路径更优,在有障碍物环境中也能合理地进行路径规划。
In view of the traditional ant colony algorithm in path planning in complex network slow convergence speed and fall into local optimal solution of the problem, based on the ant colony algorithm on the basis of state transition probability formula considering the safety factor and the weighting factor, in the process of global pheromone update introduced adaptive dynamic factor, this paper puts forward the improved adaptive ant colony algorithm to obtain the global optimal solution more quickly. Improved adaptive ant colony algorithm was applied to mobile robot path planning, image view method was used to depict the obstacles, experimental analysis, through the real data proves that the improved adaptive ant colony algorithm has a faster speed, better path than the traditional ant colony algorithm and the improved ant colony algorithm convergence, with an obstacle in the environment can reasonably make path planning.
出处
《应用数学进展》
2021年第6期2073-2082,共10页
Advances in Applied Mathematics
