  The detection and estimation of gravitational wave (GW) signals belonging to
a parameterized family of waveforms requires, in general, the numerical
maximization of a data-dependent function of the signal parameters. Due to
noise in the data, the function to be maximized is often highly multi-modal
with numerous local maxima. Searching for the global maximum then becomes
computationally expensive, which in turn can limit the scientific scope of the
search. Stochastic optimization is one possible approach to reducing
computational costs in such applications. We report results from a first
investigation of the Particle Swarm Optimization (PSO) method in this context.
The method is applied to a testbed motivated by the problem of detection and
estimation of a binary inspiral signal. Our results show that PSO works well in
the presence of high multi-modality, making it a viable candidate method for
further applications in GW data analysis.
