A Dynamic Multi-Priority Unmanned Aerial Vehicle Assignment Algorithm Integrating an Improved Discrete Particle Swarm Optimization and Greedy Strategy
TL;DR
A hybrid intelligent scheduling algorithm abbreviated as DPSO-Greedy that performs periodic global batch optimization for regular orders using an improved discrete particle swarm optimization (DPSO) method, and realizes instantaneous allocation of emergency orders via an adaptive multi-factor Greedy strategy, thus enabling efficient collaborative processing of differentiated tasks.
Abstract
To address the fundamental trade-off between real-time responsiveness to high-priority missions and long-term overall economic efficiency of the system in multi-UAV dynamic task assignment, we propose a hybrid intelligent scheduling algorithm abbreviated as DPSO-Greedy. The algorithm performs periodic global batch optimization for regular orders using an improved discrete particle swarm optimization (DPSO) method, and realizes instantaneous allocation of emergency orders via an adaptive multi-factor Greedy strategy, thus enabling efficient collaborative processing of differentiated tasks. Targeting the trade-off between real-time response and long-term system efficiency, this paper proposes a hybrid DPSO-Greedy algorithm with decoupled task scheduling mechanisms. Comparative simulation results demonstrate that compared with mainstream metaheuristic algorithms (Greedy, SSA, GWO and RHS), the proposed method reduces the average response time of emergency orders by 33.2–68.2%, achieves an emergency order completion rate exceeding 90%, and improves system load balancing performance by 24–35% in dynamic scenarios characterized by burst and tidal demands. This study provides a promising solution for dynamic UAV assignment problems and offers valuable insights for a broader range of real-time resource collaborative decision-making applications.