  In this paper we propose a new parallel algorithm for solving global
optimization (GO) multidimensional problems. The method unifies two powerful
approaches for accelerating the search: parallel computations and local tuning
on the behavior of the objective function. We establish convergence conditions
for the algorithm and theoretically show that the usage of local information
during the global search permits to accelerate solving the problem
significantly. Results of numerical experiments executed with 100 test
functions are also reported.
