Open Access

ARTICLE

Reliability-Based Multiobjective Design Optimization under Interval Uncertainty

by Fangyi Li¹, Zhen Luo³, Guangyong Sun⁴

1 School of Automotive and mechanical Engineering, Changsha University of Science and Technology, Changsha, 410114, China. Corresponding Author: Telephone: +86-731-8525-8630; Fax:+86-731-8525-8630; Email: lfy703@sina.com (Fangyi Li)
2 Key Laboratory of Manufacture and Test Techniques for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing, 400054,China.
3 School of Electrical, Mechanical and Mechatronic Systems Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
4 State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, 410082, China

Computer Modeling in Engineering & Sciences 2011, 74(1), 39-64. https://doi.org/10.3970/cmes.2011.074.039

Abstract

This paper studies the reliability-based multiobjective optimization by using a new interval strategy to model uncertain parameters. A new satisfaction degree of interval, which is significantly extended from [0, 1] to [–∞, +∞], is introduced into the non-probabilistic reliability-based optimization. Based on a predefined satisfaction degree level, the uncertain constraints can be effectively transformed into deterministic ones. The interval number programming method is applied to change each uncertain objective function to a deterministic two-objective optimization. So in this way the uncertain multiobjective optimization problem is transformed into a deterministic optimization problem and a reliability-based multiobjective optimization is then established. For sophisticated engineering problems, the objectives and constraints are modeled by using the response surface (RS) approximation method to improve the optimization efficiency. Thus the reliability-based multiobjective optimization is combined with the RS approximation models to form an approximation optimization problem. For the multiobjective optimization, the Pareto sets can be obtained with different satisfactory degree levels. Two numerical examples and one real-world crashworthiness design for vehicle frontal structure are presented to demonstrate the effectiveness of the proposed approach.

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Keywords

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