  In this work, we propose a heuristic based open source solver for finding
global solution to constrained derivative-free optimization (DFO) problems. Our
solver named Global optimization using Surrogates for Derivative-free
Optimization (GSDO) relies on surrogate approximation to the original problem.
In the proposed algorithm, an initial feasible point is first generated. This
point is subsequently used to generate well spaced feasible points for
formulating better radial basis function based surrogate approximations to
original objective and constraint functions. Finally, these surrogates are used
to solve the derivative-free global optimization problems. The proposed solver
is capable of handling quantifiable and nonquantifiable as well as relaxable
and unrelaxable constraints. We compared the performance of proposed solver
with state of the art solvers like Nonlinear Optimization by Mesh Adaptive
Direct Search (NOMAD), differential evolution (DE) and Simplicial Homology
Global Optimization (SHGO) on standard test problems. The numerical results
clearly demonstrate that the performance of our method is competitive with
respect to other solvers.
