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We propose an innovative approach to the concurrent exploration ofgravitational waves originating from Galactic binaries through the developmentof a new Local Maxima Particle Swarm Optimization (LMPSO) algorithm. Ourmethodology employs strategic Create Voids (CV) to streamline parameter space,maximizing the identification of local maxima for the $\mathcal{F}$-statisticeven in the overlapped signals case. Subsequently, a``find-real-$\mathcal{F}$-statistic-analysis", which implements theastrophysical models and properties of $\mathcal{F}$-statistic in parameterspace, is conducted to reveal Galactic binary gravitational wave signals withinthe dataset. Our new approach eliminates inaccuracies associated with signalsubtraction contamination, which is a challenge for traditionaliterative-subtraction methods when addressing low signal-to-noise ratio signals(e.g., SNR $<$ 15). To demonstrate the efficacy of our approach, we utilize theresiduals from the LISA mock data challenge (LDC1-4), where 10982 injectionsources with optimal SNR $>$ 15 have been eliminated. The LMPSO-CV methodefficiently identifies 8995 signals with a 47.7\% false source fraction or 3463signals with a 26.9\% false source fraction when the correlation coefficientthreshold is set to 0.8.

Simultaneously search for multi-target Galactic binary gravitational waves in reduced parameter space with LMPSO-CV