Response surface-based simulation optimization method is widely used in the design of complex products for its low-cost in optimization target valuation. However, when design variables increase, it often takes considerable time for high-dimensional response surface to search the optimal point, which falls easily into the local optimum due to the large search space.

To solve these problems, a GPU (Graphic Processing Unit) parallel optimization based on branch and bound is proposed in this paper, of which the main algorithm flow can be described as the following steps: the optimization space is branched to subsets and mapped to different GPU threads; the Chebyshev response surface is constructed within the threads; the compact convex hull of the subsets are obtained through the interval operation, and the optimization space is reduced on a large scale by pruning; all subsets that may contain optimal design points are efficiently obtained by repeating spatial subdivision and demarcation; finally, all the reserved subsets are mapped to different GPU threads, and all global optimization design points are obtained through sequential quadratic programming and comparative analysis.

基于响应面的仿真优化方法以其低成本的优化目标价值而被广泛用于复杂产品的设计中。但是，当设计变量增加时，高维响应面通常会花费大量时间来搜索最优点，由于搜索空间较大，该最优点很容易落入局部最优点。

为了解决这些问题，本文提出了一种基于分支定界的GPU（图形处理单元）并行优化方法，其主要算法流程可分为以下几个步骤：优化空间分支到子集并映射到不同的子集。 GPU线程；切比雪夫响应面构造在螺纹内；通过区间运算得到子集的紧凑凸包，并通过修剪大幅度减少优化空间。通过重复空间细分和划分，可以有效地获得所有可能包含最佳设计点的子集；最后，将所有保留的子集映射到不同的GPU线程，并通过顺序二次编程和比较分析获得所有全局优化设计点。