Resilient Task Allocation for UAV Swarms: A Bilevel PSO-ILP Optimization Approach

To address the severe challenges of task allocation for UAV swarms in uncertain complex environments, this paper introduces the concept of equivalent load, constructs the load capability matrix of a single UAV and the task required load matrix of the task area, and designs a new task resilience capability indicator accordingly to conduct research on a resilience-based optimization framework. Aiming at this multi-objective optimization problem, the “Problem Decomposability Theorem” is proposed, which theoretically proves the feasibility of decomposing the UAV swarm problem into “lower-level Integer Linear Programming (ILP) cost optimization” and “upper-level Particle Swarm Optimization (PSO) resilience optimization”. Based on this, a Particle Swarm Optimization–Integer Linear Programming (PSO-ILP) two-layer nested optimization algorithm is designed. Simulation experiments covering three task areas, five payload types and multiple UAV types are carried out, and the results show that the proposed method has outstanding performance in multi-objective optimization, especially in terms of algorithm convergence and the comprehensive efficiency of swarm load cost and task resilience. In particular, when the interruption probability is in the range of 0.2 to 0.6, it can not only maintain high task resilience but also achieve cost minimization, with a significant improvement in resilience performance. These results not only enrich the theoretical research on UAV swarm resilience but also provide a universal solution for UAV swarm task optimization in multiple fields.