  Current strategies employed for maritime target search and tracking are
primarily based on the use of agents following a predetermined path to perform
a systematic sweep of a search area. Recently, dynamic Particle Swarm
Optimization (PSO) algorithms have been used together with swarming multi-robot
systems (MRS), giving search and tracking solutions the added properties of
robustness, scalability, and flexibility. Swarming MRS also give the end-user
the opportunity to incrementally upgrade the robotic system, inevitably leading
to the use of heterogeneous swarming MRS. However, such systems have not been
well studied and incorporating upgraded agents into a swarm may result in
degraded mission performances. In this paper, we propose a PSO-based strategy
using a topological k-nearest neighbor graph with tunable exploration and
exploitation dynamics with an adaptive repulsion parameter. This strategy is
implemented within a simulated swarm of 50 agents with varying proportions of
fast agents tracking a target represented by a fictitious binary function.
Through these simulations, we are able to demonstrate an increase in the
swarm's collective response level and target tracking performance by
substituting in a proportion of fast buoys.
