Biobjective Optimization Algorithms Using Neumann Series Expansion for Engineering Design. (19th December 2018)

• Record Type: Journal Article
• Authors: Guo, Huan; Tatsuo, Yoshino; Fan, Lulu; Ding, Ao; Xu, Tianshuang; Xing, Genyuan
• Other Names: Merodio Jose Academic Editor.
• Is Part Of: Applied bionics and biomechanics. Volume 2018(2018)
• Publication Date: 2018-12-19
• Subjects: Bionics -- Periodicals; Biomechanics -- Periodicals; Biomedical engineering -- Periodicals; 003.505
• DOI: 10.1155/2018/7071647
• Languages: English
• ISSNs: 1176-2322

Abstract

Abstract : In this paper, two novel algorithms are designed for solving biobjective optimization engineering problems. In order to obtain the optimal solutions of the biobjective optimization problems in a fast and accurate manner, the algorithms, which have combined Newton's method with Neumann series expansion as well as the weighted sum method, are applied to deal with two objectives, and the Pareto optimal front is achieved through adjusting weighted factors. Theoretical analysis and numerical examples demonstrate the validity and effectiveness of the proposed algorithms. Moreover, an effective biobjective optimization strategy, which is based upon the two algorithms and the surrogate model method, is developed for engineering problems. The effectiveness of the optimization strategy is proved by its application to the optimal design of the dummy head structure in the car crash experiments.