  In the paper, the global optimization problem of a multidimensional
"black-box" function satisfying the Lipschitz condition over a hyperinterval
with an unknown Lipschitz constant is considered. A new efficient algorithm for
solving this problem is presented. At each iteration of the method a number of
possible Lipschitz constants is chosen from a set of values varying from zero
to infinity. This idea is unified with an efficient diagonal partition
strategy. A novel technique balancing usage of local and global information
during partitioning is proposed. A new procedure for finding lower bounds of
the objective function over hyperintervals is also considered. It is
demonstrated by extensive numerical experiments performed on more than 1600
multidimensional test functions that the new algorithm shows a very promising
performance.
