  Individual agents in natural systems like flocks of birds or schools of fish
display a remarkable ability to coordinate and communicate in local groups and
execute a variety of tasks efficiently. Emulating such natural systems into
drone swarms to solve problems in defence, agriculture, industry automation and
humanitarian relief is an emerging technology. However, flocking of aerial
robots while maintaining multiple objectives, like collision avoidance, high
speed etc. is still a challenge. In this paper, optimized flocking of drones in
a confined environment with multiple conflicting objectives is proposed. The
considered objectives are collision avoidance (with each other and the wall),
speed, correlation, and communication (connected and disconnected agents).
Principal Component Analysis (PCA) is applied for dimensionality reduction, and
understanding the collective dynamics of the swarm. The control model is
characterised by 12 parameters which are then optimized using a multi-objective
solver (NSGA-II). The obtained results are reported and compared with that of
the CMA-ES algorithm. The study is particularly useful as the proposed
optimizer outputs a Pareto Front representing different types of swarms which
can applied to different scenarios in the real world.
