  Drones are effective for reducing human activity and interactions by
performing tasks such as exploring and inspecting new environments, monitoring
resources and delivering packages. Drones need a controller to maintain
stability and to reach their goal. The most well-known drone controllers are
proportional-integral-derivative (PID) and proportional-derivative (PD)
controllers. However, the controller parameters need to be tuned and optimized.
In this paper, we introduce the use of two evolutionary algorithms,
biogeography-based optimization~(BBO) and particle swarm optimization (PSO),
for multi-objective optimization (MOO) to tune the parameters of the PD
controller of a drone. The combination of MOO, BBO, and PSO results in various
methods for optimization: vector evaluated BBO and PSO, denoted as VEBBO and
VEPSO; and non-dominated sorting BBO and PSO, denoted as NSBBO and NSPSO. The
multi-objective cost function is based on tracking errors for the four states
of the system. Two criteria for evaluating the Pareto fronts of the
optimization methods, normalized hypervolume and relative coverage, are used to
compare performance. Results show that NSBBO generally performs better than the
other methods.
