A New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM)
This addresses the challenge of efficient global optimization in multidimensional search spaces, though it appears incremental as it builds on subdivision methods.
The paper tackles the problem of high computational cost in global optimization by proposing the Subdividing Labeling Method (SLM), which achieves faster and more reliable results with optimal time complexity O(logn) compared to existing techniques like random search and simulated annealing.
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).