# How many generations does it typically take for a differential evolution method to reach a global optimum?

For differential evolution methods in optimization, how many generations does it typically take to reach a global optimum?

How do we know if the values are never going to converge?

I don't think there's enough information to give a heuristic like, "in general, it takes $k$ iterations for differential evolution to reach a global optimum".
First, we don't know how many iterations deterministic global optimizers will take to converge to global $\varepsilon$-optimality in the general case. In limited special cases, we know that certain optimization algorithms will converge within a certain number of steps (e.g., simplex algorithms for linear programs, branch-and-bound algorithms for mixed-integer linear programs, polynomial integer programs, Newton methods for unconstrained convex quadratic programs, conjugate gradient methods for unconstrained convex quadratic programs, Newton methods for unconstrained convex nonlinear programs inside the quadratic region of convergence).