Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (4): 1399-1412.doi: 10.23919/JSEE.2026.000021

• CONTROL THEORY AND APPLICATION • Previous Articles    

Hybrid DE-ACO for multi-UAV task allocation with constrained path planning

Nuo Xu1(), Bei Huang2(), Qian Zhu2(), Chaoyang Dong1,*(), Changjian Zhao2   

  1. 1School of Astronautics Science and Engineering, Beihang University, Beijing 100191, China
    2China Academy of Launch Vehicle Technology, Beijing 100076, China
  • Received:2025-07-23 Online:2026-08-18 Published:2026-09-03
  • Contact: Chaoyang Dong E-mail:xunuo@buaa.edu.cn;HuangBei_Calt@163.com;zhuqian@buaa.edu.cn;dongchaoyang@buaa.edu.cn

Abstract:

This paper proposes a hybrid differential evolution and ant colony optimization (DE-ACO) algorithm for multi-unmanned aerial vehicle (UAV) task allocation and path planning, addressing dynamic task allocation in heterogeneous UAV clusters. The algorithm integrates task order constraints, uniform target point weights, and path planning to avoid terrain obstacles and radar threats, ensuring efficient resource utilization. Simulations demonstrate balanced task allocation, rapid convergence, and robustness under UAV failure and communication range reduction scenarios. Compared to a baseline ant colony optimization approach, the hybrid DE-ACO method offers enhanced flexibility, making it a promising solution for real-world multi-UAV missions.

Key words: differential evolution and ant colony optimization (DE-ACO), multi-unmanned aerial vehicle (UAV) task allocation, task quantity constraint, dynamic parameter adjustment, network robustness