  We propose and implement a novel hybrid meta-heuristic optimization algorithm
for the identification of non-collinear global ground-states in magnetic
systems. The inputs to this optimization scheme are directly from non-collinear
density functional theory (DFT), and the workflow is implemented in the atomate
code framework, making it suitable to run on high-performance computing
architectures. The hybrid algorithm provides a seamless theoretical extension
of particle swarm optimization (PSO) algorithms to continuous $\mathcal S^2$
spins, giving it the name SpinPSO. The hybrid nature of the algorithm stems
from setting the dynamics of individual spins to be governed by physically
motivated atomistic spin dynamics. Using this algorithm, we are able to achieve
convergence to experimentally resolved magnetic ground-states for a set of
diverse test case materials that exhibit exotic spin textures.
