@article{FENG2022, 
author = {Qiang FENG and Xingshuo HAI and Bo SUN and Yi REN and Zili WANG and Dezhen YANG and Yaolong HU and Ronggen FENG},
title = {Resilience optimization for multi-UAV formation reconfiguration via enhanced pigeon-inspired optimization},
year = {2022},
journal = {Chinese Journal of Aeronautics},
volume = {35},
number = {1},
pages = {110-123},
keywords = {Formation reconfiguration, Parameter optimization, Pigeon-inspired optimization, Resilience, Unmanned aerial vehicles},
url = {https://www.sciopen.com/article/10.1016/j.cja.2020.10.029},
doi = {10.1016/j.cja.2020.10.029},
abstract = {This paper develops a novel optimization method oriented to the resilience of multiple Unmanned Aerial Vehicle (multi-UAV) formations to achieve rapid and accurate reconfiguration under random attacks. First, a resilience metric is applied to reflect the effect and rapidity of multi-UAV formation resisting random attacks. Second, an optimization model based on a parameter optimization problem to maximize the system resilience is established. Third, an Adaptive Learning-based Pigeon-Inspired Optimization (ALPIO) algorithm is designed to optimize the resilience value. Finally, typical formation topologies with six UAVs are investigated as a case study to verify the proposed approach. The experimental results indicate that the proposed scheme can achieve resilience optimization for a multi-UAV formation reconfiguration by increasing the system resilience values to 97.53% and 81.4% after random attacks.}
}