Metamodel-based design optimization employing a novel sequential sampling strategy. Issue 8 (6th November 2017)

• Record Type: Journal Article
• Authors: Shu, Leshi; Jiang, Ping; Wan, Li; Zhou, Qi; Shao, Xinyu; Zhang, Yahui
• Is Part Of: Engineering computations. Volume 34:Issue 8(2017)
• Page Start: 2547
• Page End: 2564
• Publication Date: 2017-11-06
• Subjects: Metamodel -- Global optimization -- Sequential sampling -- Metamodel accuracy -- Sampling strategy; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-01-2016-0034
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization. Design/methodology/approach: A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed. Findings: The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum. Originality/value: The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.