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

A New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM)