  Management and mission planning over a swarm of unmanned aerial vehicle (UAV)
remains to date as a challenging research trend in what regards to this
particular type of aircrafts. These vehicles are controlled by a number of
ground control station (GCS), from which they are commanded to cooperatively
perform different tasks in specific geographic areas of interest.
Mathematically the problem of coordinating and assigning tasks to a swarm of
UAV can be modeled as a constraint satisfaction problem, whose complexity and
multiple conflicting criteria has hitherto motivated the adoption of
multi-objective solvers such as multi-objective evolutionary algorithm (MOEA).
The encoding approach consists of different alleles representing the decision
variables, whereas the fitness function checks that all constraints are
fulfilled, minimizing the optimization criteria of the problem. In problems of
high complexity involving several tasks, UAV and GCS, where the space of search
is huge compared to the space of valid solutions, the convergence rate of the
algorithm increases significantly. To overcome this issue, this work proposes a
weighted random generator for the creation and mutation of new individuals. The
main objective of this work is to reduce the convergence rate of the MOEA
solver for multi-UAV mission planning using weighted random strategies that
focus the search on potentially better regions of the solution space. Extensive
experimental results over a diverse range of scenarios evince the benefits of
the proposed approach, which notably improves this convergence rate with
respect to a na\"ive MOEA approach.
