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改进蚁群算法的移动机器人路径规划
引用本文:唐旭晖,辛绍杰.改进蚁群算法的移动机器人路径规划[J].计算机工程与应用,2022,58(5):287-295.
作者姓名:唐旭晖  辛绍杰
作者单位:上海电机学院 机械学院,上海 201306
摘    要:针对传统蚁群算法在路径规划中存在易陷入局部最优与收敛速度慢等问题,提出一种改进的蚁群算法.采用初始信息素差异化分布策略,增强目标点导向区的初始信息素浓度;基于回退策略与禁忌搜索结合分块优化,利用叉积运算进行局部折点优化;引入信息素自调节加强因子,改进信息素浓度更新公式;引入随机状态转移参数,增强全局搜索能力;将改进算法...

关 键 词:蚁群算法  路径规划  差异化分布策略  叉积运算  自调节加强因子  随机状态转移参数

Improved Ant Colony Algorithm for Mobile Robot Path Planning
TANG Xuhui,XIN Shaojie.Improved Ant Colony Algorithm for Mobile Robot Path Planning[J].Computer Engineering and Applications,2022,58(5):287-295.
Authors:TANG Xuhui  XIN Shaojie
Affiliation:College of Mechanical, Shanghai Dianji University, Shanghai 201306, China
Abstract:An improved ant colony algorithm is proposed to address the problems of the traditional ant colony algorithm in path planning,such as the tendency to fall into local optimum and slow convergence speed.The initial pheromone concentration in the target point guidance area is enhanced by adopting the initial pheromone differential distribution strategy.The local fold optimization is carried out by using the fork product operation based on the backoff strategy and taboo search combined with chunking optimization.The pheromone self-adjustment enhancement factor is introduced to improve the pheromone concentration update formula.The random state transfer parameter is introduced to enhance the global search capability.The improved algorithm is compared with the traditional ant colony algorithm and the salp swarm algorithm in a variety of map environments.The simulation results prove that the improved algorithm has better convergence and stability in path planning.
Keywords:ant colony algorithm  path planning  differential distribution strategy  fork product operation  self-adjustment enhancement factor  random state transfer parameter
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