《计算机应用研究》|Application Research of Computers

DEM中基于遗传与蚁群的混合路径规划算法

Hybrid path planning algorithm based on genetics and ant colony in DEM

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作者 武小年,奚玉昂,张润莲
机构 桂林电子科技大学 a.广西可信软件重点实验室;b.广西密码学与信息安全重点实验室;c.广西高校云计算与复杂系统重点实验室,广西 桂林 541004
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文章编号 1001-3695(2020)09-026-2694-04
DOI 10.19734/j.issn.1001-3695.2019.04.0133
摘要 现有启发式算法在DEM路径规划中因数据量巨大导致效率较低。针对该问题,提出一种基于遗传和蚁群的混合路径规划算法。该算法在遗传过程中,通过在初始群体生成阶段构建选择因子,使得在节点搜索时更加倾向于终点方向,提高初始群体生成效率;对变异过程中变异节点的变异区间进行限制,避免产生路径断点;在蚁群寻优过程中,根据遗传过程产生的路径信息,采用自适应信息素初始化与更新策略,提高算法搜索效率。测试结果表明,混合算法能够在规则网格DEM数据下搜索出符合条件的路径,并具有较好的效率。
关键词 路径规划; 数字高程模型; 遗传算法; 蚁群算法
基金项目 广西自然科学基金资助项目(2018GXNSFAA294036,2018GXNSFAA138116)
广西可信软件重点实验室基金资助项目(kx201622)
广西密码学与信息安全重点实验室项目(GCIS201705,GCIS201623)
广西高校云计算与复杂系统重点实验室项目(YF16205)
广西研究生教育创新计划资助项目(YCSW2018138,2017YJCX26)
本文URL http://www.arocmag.com/article/01-2020-09-026.html
英文标题 Hybrid path planning algorithm based on genetics and ant colony in DEM
作者英文名 Wu Xiaonian, Xi Yu'ang, Zhang Runlian
机构英文名 a.Guangxi Key Laboratory of Trusted Software,b.Guangxi Key Laboratory of Cryptography & Information Security,c.Guangxi Colleges Key Laboratory of Cloud Computing & Complex Systems,Guilin Guangxi 541004,China
英文摘要 The existing heuristic algorithm has low efficiency in the DEM path planning because of the large amount of data. In order to solve this problem, this paper proposed a hybrid path planning algorithm based on genetic algorithm and ant colony algorithm. In the genetic process, the algorithm constructed the selection factor in the initial population generation stage, which made the searched node more incline to the end direction and improved the efficiency of the initial population generation. And the algorithm restricted the variation interval of the mutation node during the mutation process to avoid the path break point. In the ant colony optimization process, according to the path information generated by the genetic process, it adopted the adaptive pheromone initialization and updated strategies to improve the algorithm search efficiency. Simulation experiments show that the hybrid algorithm can search the qualified path efficiently under the rule grid DEM.
英文关键词 path planning; digital elevation model; genetic algorithm; ant colony algorithm
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收稿日期 2019/4/2
修回日期 2019/5/9
页码 2694-2697
中图分类号 TP18
文献标志码 A