自适应多启发蚁群算法的无人机路径规划
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国防基础计划项目; 河北省重点研发计划项目(19250801D); 河北省研究生创新资助项目(CXZZSS2020098); 河北省军民融合发展研究课题(HB19JMRH009)


Research on UAV route planning based on adaptive multi heuristic ant colony algorithm
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    摘要:

    为了解决蚁群算法在无人机实现路径规划中容易陷入局部最优的问题,提出改进的蚁群算法。对信息素的挥发因子以及信息素进行上、下限设置,防止由于较短路径上的信息素过高以及较长路径上的信息素过低,使蚂蚁陷入局部最优,同时在多启发因素的影响下,将路径的整体长度作为决定状态转移概率的一个自适应启发函数因子,当路径长度很大时,自适应启发函数因子较小,使得蚁群选择该路径的概率减小。实验结果表明,改进的算法在路径长度上减少了6.4%,最优路径长度方差降低了85.78%,增加了对环境整体性的考虑,缩短了路径长度,降低了迭代次数,跳出局部最优。在环境复杂度加大的情况下,引入自适应启发函数因子之后的算法可以有效地选择较好的路径,为无人机路径规划提供了理论依据。

    Abstract:

    In order to solve the problem that ant colony algorithm is easy to fall into local optimum in UAV route planning, an improved ant colony algorithm was proposed. The upper and lower limits of pheromone volatilization factor and pheromone were set to prevent ants from falling into local optimum because pheromone on short path was too high or pheromone on long path was too low. At the same time, under the influence of multiple heuristic factors, the overall length of the path was taken as an adaptive heuristic function factor to determine the state transition probability. When the path length was large, the adaptive heuristic function factor was small, which reduced the probability of choosing the path by the ant colony. The experimental results show that the improved algorithm reduces the path length by 6.4% and the variance of the optimal path length by 85.78%, which increases the consideration of environmental integrity, shortens the path length, reduces the number of iterations, and jumps out of the local optimum. In the case of increasing environmental complexity, the algorithm can effectively choose a better path and provide a theoretical basis for UAV route planning after introducing the adaptive heuristic function factor.

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尹雅楠,甄 然,武晓晶,张春悦,吴学礼.自适应多启发蚁群算法的无人机路径规划[J].河北科技大学学报,2021,42(1):38-47

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  • 收稿日期:2020-09-06
  • 最后修改日期:2020-11-06
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  • 在线发布日期: 2020-12-18
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