Abstract
Aiming at the difficulties of implicit functions and high computation cost in engineering design optimization, a combined method is proposed in this paper, which takes advantage of support vector machine(SVM) and cross entropy method(CE). Used the ’Latinized’ centroidal Voronoi tessellation(LCVT) which can generate much uniform supporting points in the design variable space, a high accurate surrogate model is obtained by SVM. At the same time, the traditional cross entropy method is improved by the concepts of "global elite samples" and the "local elites samples" and a new parameter updating strategy for extracting the useful information in iteration history. To avoid trapping in the local optimum, a mutation operation is also included in the proposed method. Two numerical examples are used to illustrate the performance of the improved method superior to that of the traditional one. Finally, an engineering example is employed to demonstrate the feasibility of the proposed method in the field of engineering.