Computational Intelligence Algorithms for UAV Swarm Networking and Collaboration: A Comprehensive Survey and Future Directions

Abstract

Unmanned aerial vehicle (UAV) swarm networking and collaboration have significant prospects in both civilian and military applications, due to its remarkable properties in cooperative efficiency, reduced risks, and operational cost. Traditional algorithms have challenging issues of high computational complexity and low efficiency in UAV swarm networking and collaboration, while computational intelligence (CI) has attracted increasing attention since it has advantages in solving complex optimization problems. The networking of UAV swarms serves as an essential foundation for collaboration, and intelligent collaboration is a crucial means of enhancing the performance of UAV swarm systems. To date, extensive CI-based algorithms have been proposed to improve the networking and collaboration capabilities of UAV swarms, and several relevant surveys have also been presented. However, existing surveys either review networking or collaboration. To the best of our knowledge, there is no survey that simultaneously concentrates on CI-based UAV swarm networking and collaboration. In this survey, we provide a comprehensive overview of CI-based networking and collaboration algorithms from six typical aspects including channel access, network routing, cooperative task assignment, cooperative path planning, cooperative search, and cooperative jamming. More importantly, to help researchers choose appropriate algorithms to satisfy the requirements of different missions, we classify CI-based algorithms into four categories, namely heuristic behavior search-based algorithms, policy design-based algorithms, policy learning-based algorithms, and hybrid algorithms. Finally, we discuss open issues and future directions that may influence future research on UAV swarm intelligence networking and collaboration. This review may provide new insights and valuable references for researchers in this field.