  Most global optimization problems are nonlinear and thus difficult to solve,
and they become even more challenging when uncertainties are present in
objective functions and constraints. This paper provides a new two-stage hybrid
search method, called Eagle Strategy, for stochastic optimization. This
strategy intends to combine the random search using L\'evy walk with the
firefly algorithm in an iterative manner. Numerical studies and results suggest
that the proposed Eagle Strategy is very efficient for stochastic optimization.
Finally practical implications and potential topics for further research will
be discussed.
