A Genetic Algorithm Approach to Anti-Jamming UAV Swarm Behavior

2025-10-08

AI Summary

To address the vulnerability of UAV swarm communications to jamming attacks in adversarial environments and the insufficient robustness of conventional anti-jamming techniques, this paper proposes a genetic algorithm-based joint optimization framework. It is the first to simultaneously optimize the swarm’s physical formation configuration, reconfigurable beam-steering antenna pointing, and multi-hop communication routing strategy. By integrating realistic wireless channel modeling with multi-objective optimization, the method enables dynamic, adaptive anti-jamming operation over low-rate, high-robustness control channels. Simulation results demonstrate significant improvements in primary coordination channel stability and link survivability under jamming, effectively mitigating interference impact. The core contribution lies in internalizing swarm intelligence as a tightly coupled communication-sensing-control co-optimization mechanism—thereby overcoming the traditional decoupling between physical-layer anti-jamming and network-layer cooperative scheduling. A key limitation is the relatively high computational overhead.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Adversarial Search
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In recent years, Unmanned Aerial Vehicles (UAVs) have brought a new true revolution to military tactics. While UAVs already constitute an advantage when operating alone, multi-UAV swarms expand the available possibilities, allowing the UAVs to collaborate and support each other as a team to carry out a given task. This entails the capability to exchange information related with situation awareness and action coordination by means of a suitable wireless communication technology. In such scenario, the adversary is expected to disrupt communications by jamming the communication channel. The latter becomes the Achilles heel of the swarm. While anti-jamming techniques constitute a well covered topic in the literature, the use of intelligent swarm behaviors to leverage those techniques is still an open research issue. This paper explores the use of Genetic Algorithms (GAs) to jointly optimize UAV swarm formation, beam-steering antennas and traffic routing in order to mitigate the effect of jamming in the main coordination channel, under the assumption that a more robust and low data rate channel is used for formation management signaling. Simulation results show the effectiveness of proposed approach. However, the significant computational cost paves the way for further research.

Problem

• Optimizing UAV swarm formation to resist jamming attacks
• Jointly improving beam-steering antennas against communication disruption
• Enhancing traffic routing to mitigate jamming in coordination channels

Innovation

• Genetic Algorithms optimize UAV swarm formation
• Beam-steering antennas enhance anti-jamming communication
• Joint optimization of traffic routing and formation