Global optimization method using ensemble of metamodels based on fuzzy clustering for design space reduction

For most engineering design optimization problems, it is difficult or even impossible to find the global optimum due to the unaffordable computational cost. To overcome this difficulty, a global optimization method that integrates the ensemble of metamodels and fuzzy clustering is proposed to deal with the optimization problems involving the computation-intensive, black-box computer analysis and simulation. The ensemble of metamodels combining three representative metamodeling techniques with optimized weight factors is used to decrease the computational expense during the optimization procedure and the fuzzy clustering technique is applied to obtain the reduced design space. In this way, the efficiency and capability of capturing the global optimum will be improved in the reduced design space. To demonstrate the superior performance of the proposed global optimization method over existing methods, it is examined using various benchmark optimization problems and applied to solve an engineering design optimization problem. The results show that the proposed global optimization method is robust and efficient in capturing the global optimum.