A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs

This research offers an improved method for the self-organization of a swarm UAVs based on social learning approach. To start, we use three different colonies and best members i.e., unmanned aerial vehicles (UAVs) randomly placed in colonies. study uses max-min ant colony optimization (MMACO) conjunction with mechanism to plan optimized path individual colony. Hereinafter, multi-agent system (MAS) chooses most optimal UAV as leader each remaining agents, which helps organize positioned into formations. Afterward, algorithm synchronizes connects controls it using dynamic selection. The major contribution this is hybridize two approaches produce more optimized, efficient, effective strategy. results verify that proposed completes given objectives. also compares designed Non-Dominated Sorting Genetic Algorithm II (NSGA-II) prove our better convergence reaches target shorter route than NSGA-II.