  This research concerns design optimization problems involving numerous design
parameters and large computational models. These problems generally consist in
non-convex constrained optimization problems in large and sometimes complex
search spaces. The classical simulated annealing algorithm rapidly loses its
efficiency in high search space dimension. In this paper a variant of the
classical simulated annealing algorithm is constructed by incorporating (1) an
It\^o stochastic differential equation generator (ISDE) for the transition
probability and (2) a polyharmonic splines interpolation of the cost function.
The control points are selected iteratively during the research of the optimum.
The proposed algorithm explores efficiently the design search space to find the
global optimum of the cost function as the best control point. The algorithm is
illustrated on two applications. The first application consists in a simple
function in relatively high dimension. The second is related to a Finite
Element model.
