CLC number: TP242.6; V279
On-line Access: 2020-05-18
Received: 2020-02-10
Revision Accepted: 2020-03-30
Crosschecked: 2020-04-13
Cited: 0
Clicked: 5090
Citations: Bibtex RefMan EndNote GB/T7714
Wan-ying Ruan, Hai-bin Duan. Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000066 @article{title="Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization", %0 Journal Article TY - JOUR
基于多目标社会学习鸽群优化的多无人机避障控制1北京航空航天大学自动化科学与电气工程学院,虚拟现实技术与系统国家重点实验室,中国北京市,100083 2鹏城实验室,中国深圳市,518000 摘要:提出多目标社会学习鸽群优化(MSLPIO)方法,将其应用于无人机编队避障控制。该算法特点在于,每只鸽子在更新过程中并非向全局最优的鸽子学习,而是学习比自己占优的任何鸽子。在地图指南针算子和地标算子中引入社会学习因子。此外,为避免参数设置的盲目性,采用维数相关的参数设置方法。本文模拟了5架飞机在复杂障碍环境下的飞行过程,实验结果验证了该方法的有效性。与改进的多目标鸽群优化算法和改进的非占优排序遗传算法相比,MSLPIO具有更好的收敛性。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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