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Technical Briefs

Multi-Objective Collaborative Optimization Based on Evolutionary Algorithms

[+] Author and Article Information
Su Ruiyi1

 Beijing System Design Institute of Electromechanical Engineering, No. 31 Yongding Road, Haidian District, Beijing 100854, Chinasry@mails.tsinghua.edu.cn

Gui Liangjin

State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering,  Tsinghua University, Beijing 100084, Chinagui@mails.tsinghua.edu.cn

Fan Zijie

State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering,  Tsinghua University, Beijing 100084, Chinazjfan@mails.tsinghua.edu.cn

1

Corresponding author.

J. Mech. Des 133(10), 104502 (Oct 18, 2011) (5 pages) doi:10.1115/1.4004970 History: Received January 13, 2011; Revised August 25, 2011; Published October 18, 2011; Online October 18, 2011

This paper proposes a novel multi-objective collaborative optimization (MOCO) approach based on multi-objective evolutionary algorithms for complex systems with multiple disciplines and objectives, especially for those systems in which most of the disciplinary variables are shared. The shared variables will conflict when the disciplinary optimizers are implemented concurrently. In order to avoid the confliction, the shared variables are treated as fixed parameters at the discipline level in most of the MOCO approaches. But in this paper, a coordinator is introduced to handle the confliction, which allocates more design freedom and independence to the disciplinary optimizers. A numerical example is solved, and the results are discussed.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 4

Nondominated optimal solutions obtained by AIO, MOCO1, and MOCO2 (the best run among 30 runs)

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Figure 3

The system of the example

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Figure 2

Formulation of proposed MOCO

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Figure 1

A fully coupled three-discipline nonhierarchic system

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