吉林大学学报(理学版) ›› 2022, Vol. 60 ›› Issue (5): 1123-1132.

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基于自适应樽海鞘算法的多无人机任务分配

张森悦1, 隋学梅2, 李一波2   

  1. 1. 沈阳航空航天大学 人工智能学院, 沈阳 110136; 2. 沈阳航空航天大学 自动化学院, 沈阳 110136
  • 收稿日期:2021-11-16 出版日期:2022-09-26 发布日期:2022-09-26
  • 通讯作者: 张森悦 E-mail:zhang_senyue@sau.edu.cn

Multi-UAV Task Assignment Based on Adaptive Salps Swarm Algorithm

ZHANG Senyue1, SUI Xuemei2, LI Yibo2   

  1. 1.   School of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China;
    2.  School of Automation, Shenyang Aerospace University, Shenyang 110136, China
  • Received:2021-11-16 Online:2022-09-26 Published:2022-09-26

摘要: 针对多无人机的任务分配问题, 提出一种基于自适应樽海鞘算法的多无人机任务分配方法. 在经典樽海鞘算法的基础上, 重新设计领导者的位置更新公式, 以改善樽海鞘算法易陷入局部最优的缺陷, 同时在算法迭代过程中加入自适应算子, 对领导者和跟随者的数量进行动态调整, 以提高算法前期的全局搜索和后期跳出局部极值的能力. 通过与遗传算法、 粒子群优化算法、 经典樽海鞘算法进行对比实验, 实验结果表明, 该算法对解决多无人机任务分配问题效果较好, 具有更优的适应度和收敛性.

关键词: 多无人机, 任务分配, 自适应樽海鞘算法, 遗传算法

Abstract: Aiming at the problem of multi\|unmanned aerial vehicle (multi-UAV) task allocation, we proposed a multi-UAV task assignment method based on adaptive salps swarm algorithm. On the basis of the classic salps swarm algorithm, the leader’s position updating formula was redesigned to improve the defect that salps swarm algorithm was easy to fall into local optimality. At the same time, adaptive operators were added during  iterative process of the algorithm to dynamically adjust the number of leaders and followers, so as  to improve the global search  in the early stage and the ability to jump out of local extremes in the later stage. Through the comparative experiments with genetic algorithm, particle swarm optimization algorithm and the classical salps swarm algorithm, the experimental results show that the proposed algorithm has better effect on solving the problem of multi-UAV task assignment, and has better adaptability and convergence.

Key words: multi-unmanned aerial vehicle, task assignment, adaptive salps swarm algorithm, genetic algorithm

中图分类号: 

  • TP391