基于改进鸽群优化算法的多无人机目标搜索

计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (03): 531-535.

基于改进鸽群优化算法的多无人机目标搜索

凌文通,倪建军,陈颜，唐广翼

（河海大学物联网工程学院，江苏常州 213000）

收稿日期:2020-09-25 修回日期:2020-12-01 接受日期:2022-03-25 出版日期:2022-03-25 发布日期:2022-03-24
基金资助:
国家自然科学基金（61873086）

Multi-UAV target search based on improved pigeon swarm algorithm

LING Wen-tong,NI Jian-jun,CHEN Yan,TANG Guang-yi

(College of Internet of Things Engineering,Hohai University,Changzhou 213000,China)

Received:2020-09-25 Revised:2020-12-01 Accepted:2022-03-25 Online:2022-03-25 Published:2022-03-24

摘要/Abstract

摘要： 在三维未知环境中无人机目标搜索是一项非常具有挑战性和现实性意义的任务。鸽群优化算法相比于其他智能算法收敛速度快，搜索效率高，适用于目标优化任务，因此提出一种基于鸽群优化算法的多无人机目标搜索方法，无人机通过搜索目标留下的信息素搜寻目标。针对鸽群优化算法容易陷入局部最优的问题，利用基于差分进化策略对鸽群优化算法进行改进。仿真实验验证了提出的基于改进鸽群优化算法的多无人机目标搜索方法的合理性和有效性。

关键词: 多无人机, 目标搜索, 鸽群优化, 差分进化

Abstract: The problem of UAV target search in a three-dimensional unknown environment is a very challenging and realistic task. Compared with other intelligent algorithms, the pigeon swarm algorithm has faster convergence speed, higher search efficiency, and is suitable for target optimization tasks. Therefore, a multi-UAV target search method based on the pigeon swarm algorithm is proposed. The UAVs search for the information left by the target. Aiming at the problem that the pigeon swarm algorithm is easy to fall into the local optimum, an improved pigeon swarm algorithm based on the differential evolution strategy is proposed. Finally, simulation experiments verify the rationality and effectiveness of the proposed algorithm.

Key words: multi-UAV, target search, pigeon-inspired optimization, differential evolution

凌文通, 倪建军, 陈颜, 唐广翼. 基于改进鸽群优化算法的多无人机目标搜索[J]. 计算机工程与科学, 2022, 44(03): 531-535.

LING Wen-tong, NI Jian-jun, CHEN Yan, TANG Guang-yi. Multi-UAV target search based on improved pigeon swarm algorithm[J]. Computer Engineering & Science, 2022, 44(03): 531-535.