  The task of searching for and tracking of multiple targets is a challenging
one. However, most works in this area do not consider evasive targets that move
faster than the agents comprising the multi-robot system. This is due to the
assumption that the movement patterns of such targets, combined with their
excessive speed, would make the task nearly impossible to accomplish. In this
work, we show that this is not the case and we propose a decentralized search
and tracking strategy in which the level of exploration and exploitation
carried out by the swarm is adjustable. By tuning a swarm's exploration and
exploitation dynamics, we demonstrate that there exists an optimal balance
between the level of exploration and exploitation performed. This optimum
maximizes its tracking performance and changes depending on the number of
targets and the targets' movement profiles. We also show that the use of
agent-based memory is critical in enabling the tracking of an evasive target.
The obtained simulation results are validated through experimental tests with a
decentralized swarm of six robots tracking a virtual fast-moving target.
