Comparative Analysis of Transfer Learning and Alliance-Based Approaches in Multi-UAV Swarm Coordination and Control

Abstract

This paper provides a comparative study of two state of the art methods for coordinated control and swarm formation of unmanned aerial vehicle (UAV). The first method uses a transfer learning-based multi-objective optimization approach in which a hybrid Pigeon-Inspired Optimization (PIO) algorithm combined with transfer learning (TL) facilitates dynamic UAV clusters to execute intelligent path planning, agent swapping, and global synchronization in complex 3D environments. The second approach is based on an alliance-based formation control strategy where an alliance-based formation approach is presented that offers swarm robustness by dividing the UAV network into versatile subgroups driven by consensus laws and thus ensuring resilience in partial communication failures and dynamic topologies. Both methods are compared in this study in various aspects such as system modeling, communication strategy, fault tolerance, reconfiguration ability, optimization performance, and computational complexity. The analysis not only identifies the merits and limitations of each approach but places them in the general research agenda of collective motion in multi-agent systems. The work concludes with potential to integrate transfer learning and alliance-based resilience mechanisms into next-generation adaptive control architectures.