  In most global optimization problems, finding global optimal point inthe
multidimensional and great search space needs high computations. In this paper,
we present a new approach to find global optimal point with the low computation
and few steps using subdividing labeling method (SLM) which can also be used in
the multi-dimensional and great search space. In this approach, in each step,
crossing points will be labeled and complete label polytope search space of
selected polytope will be subdivided after being selected. SLM algorithm finds
the global point until h (subdivision function) turns into zero. SLM will be
implemented on five applications and compared with the latest techniques such
as random search, random search-walk and simulated annealing method. The
results of the proposed method demonstrate that our new approach is faster and
more reliable and presents an optimal time complexity O (logn).
