Resilience optimization for multi-UAV formation reconfiguration via enhanced pigeon-inspired optimization
Qiang Feng, Xingshuo Hai, Bo Sun, Yi Ren, Zili Wang, Dezhen Yang, Yaolong Hu, Ronggen FENG
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.