Heterogeneous UAV Swarm Task Allocation via Hierarchy Tolerance Pigeon-Inspired Optimization

The task allocation of unmanned aerial vehicle (UAV) swarm is one of the practical problems for UAV's application and serves as the premise for tackling swarm's complex missions. By establishing a heterogeneous swarm task allocation problem featuring three types of UAVs, constraints such as UAV types, flight time and task execution sequence are considered. An objective function considering flight distance and task time of various types of UAVs is designed. Inspired by the hierarchical interaction behavior of pigeons, the hierarchical structure strategy is proposed and combined with the basic pigeon-inspired optimization (PIO) to improve the population's exploration ability. Simultaneously, the finite tolerance strategy is implemented to prevent individuals from falling into local optima due to inefficient explorations. Accordingly, hierarchy tolerance PIO (HTPIO) algorithm is proposed. Through comparing with other three algorithms across benchmark functions and two examples of swarm task allocation problem, HTPIO obtains the best results on more than half of benchmark functions and all examples. It is proved that HTPIO can effectively deal with complex optimization problems without increasing computational consumption and ensures the population always maintains a strong optimization ability throughout the process.