  In many global Optimization Problems, it is required to evaluate a global
point (min or max) in large space that calculation effort is very high. In this
paper is presented new approach for optimization problem with subdivision
labeling method (SLM) but in this method for higher dimensional has high
calculation effort. Clustering-Based Parallel Genetic Algorithm (CBPGA) in
optimization problems is one of the solutions of this problem. That the initial
population is crossing points and subdividing in each step is according to
mutation. After labeling all of crossing points, selecting is according to
polytope that has complete label. In this method we propose an algorithm, based
on parallelization scheme using master-slave. SLM algorithm is implemented by
CBPGA and compared the experimental results. The numerical examples and
numerical results show that SLMCBPGA is improved speed up and efficiency.
