A multi-objective robust optimization approach for engineering design under interval uncertainty. Issue 2 (16th April 2018)

• Record Type: Journal Article
• Authors: Zhou, Qi; Shao, Xinyu; Jiang, Ping; Xie, Tingli; Hu, Jiexiang; Shu, Leshi; Cao, Longchao; Gao, Zhongmei
• Is Part Of: Engineering computations. Volume 35:Issue 2(2018)
• Page Start: 580
• Page End: 603
• Publication Date: 2018-04-16
• Subjects: Multi-objective optimization -- Sequential sampling -- Robust optimization -- Interpolation uncertainty -- Interval uncertainty; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-09-2016-0320
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. This paper aims to propose a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) to obtain the robust Pareto set under the interval uncertainty. Design/methodology/approach: In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the prediction accuracy of the Kriging metamodel during the robust optimization process. Findings: Five numerical and engineering cases are used to demonstrate the applicability of the proposed approach. The results illustrate that K-MORO is able to obtain robust Pareto frontier, while significantly reducing computational cost. Practical implications: The proposed approach exhibits great capability for practical engineering design optimization problems that are multi-objective and constrained and have uncertainties. Originality/value: A K-MORO approach is proposed, which can obtain the robust Pareto set under the interval uncertainty and ease the computational burden of the robust optimization process.