An efficient graphic processing unit parallel optimal point searching approach on complex product response surface

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.