自适应搜索半径蚁群动态路径规划算法

doi:10.3778/j.issn.1002-8331.1708-0084

计算机工程与应用 ›› 2018, Vol. 54 ›› Issue (19): 56-61.DOI: 10.3778/j.issn.1002-8331.1708-0084

自适应搜索半径蚁群动态路径规划算法

赵峰1，杨春曦1，陈飞1，黄凌云2，谈诚2

1.昆明理工大学化学工程学院，昆明 650500
2.昆明理工大学省部共建复杂有色金属资源清洁利用国家重点实验室，昆明 650093

出版日期:2018-10-01 发布日期:2018-10-19

Self-adjust searching radius dynamic planning algorithm based on ant colony algorithm

ZHAO Feng1, YANG Chunxi1, CHEN Fei1, HUANG Lingyun2, TAN Cheng2

1.Faculty of Chemical Engineering, Kunming University of Science and Technology, Kunming 650500, China
2.State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming 650093, China

Online:2018-10-01 Published:2018-10-19

摘要/Abstract

摘要： 针对用于路径规划的蚁群算法收敛速度慢、计算量大、对环境变化适应性低的局限性，提出了一种新型的自适应搜索半径蚁群路径规划算法。该算法可以根据环境复杂程度自动改变寻优半径，进行最优局部目标点的获取，然后调用改进蚁群算法获取局部区域内的最优路径，再重复循环获取新的最优局部目标点，直到找到全局目标点。仿真结果表明，提出的算法能够根据障碍分布情况自动选择合适的搜索半径，完成路径的动态规划，体现出良好的环境适应能力和较好的综合路径优化性能。

关键词: 蚁群算法, 局部信息, 局部目标点, 动态路径规划, 自适应半径

Abstract: Considering the limitation of classical ant colony algorithm?such as slowly convergent speed, bigger calculated amount and bad self-adaptability to time-varying environments when it is used in path planning, a new path planning method with self-adjust searching radius based on ant colony algorithm is proposed. Firstly, a suitable searching radius according different environmental complexity is chosen and the optimal local target point of the local region is found automatically. Then, the improved ant colony algorithm is called to obtain the optimal path of this region. Moreover, the new optimal local target point of the neighbor region is obtained by repeating the loop until the global target point is found. The simulation results show that the proposed algorithm can find suitable searching radius according different obstacles distribution, and then accomplish path planning with good self-adaptive capacity to environment and better total path optimization performances.

Key words: ant colony algorithm, local information, local target point, dynamic path planning, self-adjust radius

赵峰1，杨春曦1，陈飞1，黄凌云2，谈诚2. 自适应搜索半径蚁群动态路径规划算法[J]. 计算机工程与应用, 2018, 54(19): 56-61.

ZHAO Feng1, YANG Chunxi1, CHEN Fei1, HUANG Lingyun2, TAN Cheng2. Self-adjust searching radius dynamic planning algorithm based on ant colony algorithm[J]. Computer Engineering and Applications, 2018, 54(19): 56-61.

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自适应搜索半径蚁群动态路径规划算法

Self-adjust searching radius dynamic planning algorithm based on ant colony algorithm

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