  This paper proposes a theoretical framework of the grey wolf optimizer (GWO)
based on several interesting theoretical findings, involving sampling
distribution, order-1 and order-2 stability, and global convergence analysis.
In the part II of the paper, the global convergence analysis is carried out
based on the well-known stagnation assumption for simplification purposes.
Firstly, the global convergence property of the GWO under stagnation assumption
is abstracted and modelled into two propositions, corresponding to global
searching ability analysis and probability-1 global convergence analysis. Then,
the global searching ability analysis is carried out. Next, based on a
characteristic of the central moments of the new solution of the GWO under
stagnation assumption, the probability-1 global convergence property of the GWO
under stagnation assumption is proved. Finally, all conclusions are verified by
numerical simulations, and the corresponding discussions that the global
convergence property can still be guaranteed in the original GWO without
stagnation assumption are given.
