UAV Swarm Dynamic Task Allocation via Merged Coordination-Optimized Pigeon-Inspired Optimization

To tackle the dynamic assignment problem of unmanned aerial vehicle (UAV) swarms, a merged coordination-optimized pigeon-inspired optimization (MCOPIO) algorithm based on the pigeon-inspired optimization (PIO) algorithm is proposed in this paper. The algorithm disrupts the original pigeon distribution via random grouping and performs mutual learning and optimization within the new groups. After dynamic optimization, the underperforming pigeons are discarded, and the flock is reorganized. Subsequently, the two stages of the basic PIO are integrated through a dynamic factor. These improvements overcome the limitations of the basic PIO algorithm, such as insufficient global search capability, poor stability, and disconnection between the two algorithm stages. Comparative experiments are conducted with the state-of-the-art intelligent computing algorithms, such as the basic PIO, particle swarm optimization (PSO), genetic algorithm (GA), and improved consensus-based bundle algorithm (ICBBA), the comparative results verify the feasibility and effectiveness of our improved PIO for UAV swarm dynamic task allocation.