摘要
传统蚁群算法因在复杂环境中容易产生死锁,导致部分蚂蚁失效,造成效率低下,迭代次数增多。为此,提出了一种利用环境信息引入环境因子来调整启发函数的方法从而降低死锁情况的发生,增加了有效蚂蚁的数量,从整体上提高了蚁群的搜索速度,扩大了搜索范围。同时,传统蚁群算法在路径规划中仅在理想地域内寻求最短路径,而多因素环境中最短路径往往并非最优解。为解决此问题通过在不同环境中对转移概率进行加权优化在追求路径最短的基础上提出多目标路径规划,丰富了蚁群算法的实用性和现实意义。最后经仿真实验对优化算法进行验证,证明了上述优化的可行性。
Traditional ant colony algorithm is prone to cause failure by deadlocks in complex environments.A novel method is proposed to solve the problem,which adjusts the heuristic function by introducing environmental factors according to environmental information,increased the number of ants effectively,improved the search speed of ant colony,and expanded the search range.Aiming at the limitation of traditional ant colony algorithm in pursuit of shortest path in the ideal region in path planning,and the shortest path in the multi-factor environment is often not the optimal solution,the multi-objective path planning is proposed based on the weighted optimization of transition probability in different environments on the basis of the shortest path,which enriches the practicality and practical significance of the ant colony algorithm.Finally,the simulation experiment of optimization algorithm proves the feasibility of the method.
作者
马小铭
靳伍银
MA Xiao-ming;JIN Wu-yin(School of Mechanical and Electronical Engineering,Lanzhou University of Technology,Lanzhou,Gansu 730050,China)
出处
《计算技术与自动化》
2020年第4期100-105,共6页
Computing Technology and Automation
关键词
蚁群算法
避障
多目标
栅格法
路径规划
ant colony algorithm
obstacle avoidance
multi-objective
grid method
path planning
