An efficient graphic processing unit parallel optimal point searching approach on complex product response surface. (November 2020) - Li, Pu; Chen, Jinghuan; Li, Haiyan; Huang, Yunbao; Yang, Senquan; Hu, Songxi
Highlights: An efficient GPU parallel optimal point searching approach is proposed. Branch and bound is used in the proposed approach. Interval arithmetic is employed to delimit the subsets after branching. The proposed method can be works on PC with a GPU-capable graphics card. Abstract: 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.