Progress in Gravitational Wave data analysis is often limited by global optimization challenges rooted in the fitting of complex signal models. The fully coherent all-sky (FCAS) search for compact binary coalescences (CBCs), which requires optimizing the likelihood function of data from a detector network over CBC signal parameters, is particularly demanding. Despite its greater sensitivity, a real-time FCAS search has been impossible so far with traditional optimization methods using deterministic or Markovian parameter space sampling. We introduce a solution combining Particle Swarm Optimization (PSO) with GPU acceleration that achieves a \(\approx 48\) -fold speed-up over real-time analysis, and a potential latency of \(\lesssim 5\) sec, for the demanding scenario of a 4-detector network and low-mass signals. With large-scale simulations of Frequentist parameter estimation errors in an FCAS search now possible, we explore the regularization of the inverse problem in network analysis.