Application of Improved Quantum Particle Swarm Optimization Algorithm to Multi-Task Assignment for Heterogeneous UAVs

Abstract

Cooperative task assignment of unmanned aerial vehicles (UAVs) in complex scenarios is widely studied in recent years. The purpose of this paper is to explore new methods in complex scenarios. Scenarios discussed in this paper consist of the UAVs’ heterogeneity of range, speed and constraints of multi-task, time window in the task assignment process. Based on the objective function of the task completion time, mathematical model is constructed. Subsequently, the improved quantum particle swarm optimization (QPSO) algorithm is applied to solve the problem. Relationship between the particle position in the QPSO algorithm and the task allocation solution is established by the encoding and repair-based method, and feasible task allocation scheme is obtained. Large number of simulations show that the improved QPSO algorithm is effective in the heterogeneous UAVs’ multi-task assignment problem.