  A bio-inspired robust adaptive random search algorithm (BioRARSA), designed
for distributed beamforming for sensor and relay networks, is proposed in this
work. It has been shown via a systematic framework that BioRARSA converges in
probability and its convergence time scales linearly with the number of
distributed transmitters. More importantly, extensive simulation results
demonstrate that the proposed BioRARSA outperforms existing adaptive
distributed beamforming schemes by as large as 29.8% on average. This increase
in performance results from the fact that BioRARSA can adaptively adjust its
sampling stepsize via the "swim" behavior inspired by the bacterial foraging
mechanism. Hence, the convergence time of BioRARSA is insensitive to the
initial sampling stepsize of the algorithm, which makes it robust against the
dynamic nature of distributed wireless networks.
