In the last decades, several swarm-based optimization algorithms have emerged in the
scientific literature, followed by a massive increase in terms of their fields of application. Most of
the studies and comparisons are restricted to high-level languages (such as MATLAB
®
) and testing
methods on classical benchmark mathematical functions. Specifically, the employment of swarm-
based methods for solving energy-based acoustic localization problems is still in its inception and
has not yet been extensively studied. As such, the present work marks the first comprehensive study
of swarm-based optimization algorithms applied to the energy-based acoustic localization problem.
To this end, a total of 10 different algorithms were subjected to an extensive set of simulations
with the following aims: (1) to compare the algorithms’ convergence performance and recognize
novel, promising methods for solving the problem of interest; (2) to validate the importance (in
convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to
analyze the methods’ time efficiency when implemented in low-level languages and when executed
on embedded processors. The obtained results disclose the high potential of some of the considered
swarm-based optimization algorithms for the problem under study, showing that these methods
can accurately locate acoustic sources with low latency and bandwidth requirements, making them
highly attractive for edge computing paradigms.