  This paper presents a novel algorithm for a swarm of unmanned aerial vehicles
(UAVs) to search for an unknown source. The proposed method is inspired by the
well-known PSO algorithm and is called acceleration-based particle swarm
optimization (APSO) to address the source-seeking problem with no a priori
information. Unlike the conventional PSO algorithm, where the particle velocity
is updated based on the self-cognition and social-cognition information, here
the update is performed on the particle acceleration. A theoretical analysis is
provided, showing the stability and convergence of the proposed APSO algorithm.
Conditions on the parameters of the resulting third order update equations are
obtained using Jurys stability test. High fidelity simulations performed in
CoppeliaSim, shows the improved performance of the proposed APSO algorithm for
searching an unknown source when compared with the state-of-the-art particle
swarm-based source seeking algorithms. From the obtained results, it is
observed that the proposed method performs better than the existing methods
under scenarios like different inter-UAV communication network topologies,
varying number of UAVs in the swarm, different sizes of search region,
restricted source movement and in the presence of measurements noise.
