摘要
针对传统蚁群算法存在收敛速度慢、搜索效率低等问题,提出一种A^(*)扩展自适应蚁群算法.首先利用A^(*)算法在栅格环境下搜索初始路径,扩展初始路径构建优势区域,优化优势区域的初始信息素,避免蚁群算法在初期陷入盲目搜索;然后在转移概率中引入变向启发函数和参数自适应伪随机比例规则,提升算法搜索效率与收敛速度并淘汰劣质蚂蚁路径;最后采用B样条曲线对路径进行平滑.对比2种栅格环境下的仿真结果可知:所提出的算法能够有效地解决蚁群算法搜索效率低以及收敛速度过慢的问题,同时可以保证搜索路径的质量.
An A-star extended adaptive ant colony algorithm was proposed to solve the problems of slow convergence and low search efficiency.Firstly,A-star algorithm was used to search the initial path in grid environment,and the initial path was expanded to build the advantage area,the initial pheromone of the advantage area was optimized to avoid blind search at the initial stage of the algorithm.In order to improve the search efficiency and accelerate the convergence of the algorithm,the transformation heuristic and parameter adaptive pseudo random proportion were introduced to calculate the transformation probability;and the pheromone weights of the high quality ant path were updated adaptively,the inferior ant path was eliminated after ant colony classification was finished based on the average path of each iteration.Finally,B-spline curve was used to smooth the path.By comparing the results of two different grid environments,it was shown that this algorithm ensures the quality of the search path while solving the inefficiency and slowing convergence of ant colony algorithm.
作者
李鹏
何宸宇
刘琪
荣冬成
向宇翔
LI Peng;HE Chenyu;LIU Qi;RONG Dongcheng;XIANG Yuxiang(College of Automation and Electronic Information,Xiangtan University,Xiangtan 411105,China)
出处
《湖南科技大学学报（自然科学版）》
CAS
北大核心
2021年第2期85-92,共8页
Journal of Hunan University of Science And Technology：Natural Science Edition
基金
国家自然科学基金资助项目(61773330)
湖南省科技厅重点研发计划项目资助(2018GK2014)。
关键词
移动机器人
蚁群算法
路径规划
栅格法
mobile robot
ant colony algorithm
path planning
grid method
