Compact binaries in our galaxy are expected to be one of the main sources of gravitational waves for the future eLISA mission. During the mission lifetime, many thousands of galactic binaries should be individually resolved. However, the identification of the sources, and the extraction of the signal parameters in a noisy environment is a real challenge for data analysis. So far, stochastic searches have proven to be the most successful for this problem. In this work we present the first application of a swarm-based algorithm combining particle swarm optimization, differential evolution and Markov Chain Monte Carlo. We first demonstrate the effectiveness of the algorithm for the case of a single binary in a 1 mHz search bandwidth. This interesting problem gives the algorithm plenty of opportunity to fail, as it can be easier to find a strong noise peak rather than the signal itself. After a successful detection of a fictitious low frequency source, as well as the verification binary RXJ0806.3+1527, we then applied the algorithm to the detection of multiple binaries, over different search bandwidths, in the cases of low and mild source confusion. In all cases, we show that we can successfully identify the sources, and recover the true parameters within a 99\% credible interval.
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