A deterministic sequential maximin Latin hypercube design method using successive local enumeration for metamodel-based optimization. Issue 6 (2nd June 2016)

• Record Type: Journal Article
• Authors: Long, Teng; Wu, Di; Chen, Xin; Guo, Xiaosong; Liu, Li
• Is Part Of: Engineering optimization. Volume 48:Issue 6(2016)
• Page Start: 1019
• Page End: 1036
• Publication Date: 2016-06-02
• Subjects: Latin hypercube design -- design of computer experiments -- sequential sampling -- successive local enumeration -- metamodelling -- optimization; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2015.1081518
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : Space-filling and projective properties of design of computer experiments methods are desired features for metamodelling. To enable the production of high-quality sequential samples, this article presents a novel deterministic sequential maximin Latin hypercube design (LHD) method using successive local enumeration, notated as sequential-successive local enumeration (S-SLE). First, a mesh-mapping algorithm is proposed to map the positions of existing points into the new hyper-chessboard to ensure the projective property. According to the maximin distance criterion, new sequential samples are generated through successive local enumeration iterations to improve the space-filling uniformity. Through a number of comparative studies, several appealing merits of S-SLE are demonstrated: (1) S-SLE outperforms several existing LHD methods in terms of sequential sampling quality; (2) it is flexible and robust enough to produce high-quality multiple-stage sequential samples; and (3) the proposed method can improve the overall performance of sequential metamodel-based optimization algorithms. Thus, S-SLE is a promising sequential LHD method for metamodel-based optimization.