Title: Drone Squadron Optimization: a Self-adaptive Algorithm for Global Numerical Optimization,
Abstract: This paper proposes Drone Squadron Optimization, a new self-adaptive
metaheuristic for global numerical optimization which is updated online by a
hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many
algorithms used nowadays, which are nature-inspired. DSO is very flexible
because it is not related to behaviors or natural phenomena. DSO has two core
parts: the semi-autonomous drones that fly over a landscape to explore, and the
Command Center that processes the retrieved data and updates the drones'
firmware whenever necessary. The self-adaptive aspect of DSO in this work is
the perturbation/movement scheme, which is the procedure used to generate
target coordinates. This procedure is evolved by the Command Center during the
global optimization process in order to adapt DSO to the search landscape. DSO
was evaluated on a set of widely employed benchmark functions. The statistical
analysis of the results shows that the proposed method is competitive with the
other methods in the comparison, the performance is promising, but several
future improvements are planned.