4.7 Article

An on-line variable fidelity metamodel assisted Multi-objective Genetic Algorithm for engineering design optimization

期刊

APPLIED SOFT COMPUTING
卷 66, 期 -, 页码 438-448

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2018.02.033

关键词

Engineering design optimization; Fitness approximation; Variable-fidelity metamodel; Multi-Objective Genetic Algorithms; Metamodel uncertainty

资金

  1. National Natural Science Foundation of China (NSFC) [51775203, 51505163, 51721092]
  2. National Basic Research Program (973 Program) of China [2014CB046703]
  3. Fundamental Research Funds for the Central Universities, HUST [2016YXMS272]

向作者/读者索取更多资源

Population-based algorithms, which require a large number of fitness evaluations, can become computationally intractable when applied in engineering design optimization problems involving computational expensive simulations. To address this challenge, this paper proposes an on-line variable-fidelity meta model assisted Multi-Objective Genetic Algorithm (OLVFM-MOGA) approach. In OLVFM-MOGA, the variable-fidelity metamodel (VFM) is constructed to replace the expensive simulation models to ease the computational burden. Besides, a novel model updating strategy is developed to address the issues of 1) which sample points should be sent for simulation analysis to improve the optimization quality, and 2) whether the low-fidelity (LF) model or the high-fidelity (HF) model should be selected to run for a selected sample point. Six numerical examples and an engineering case with different degrees of complexity are used to demonstrate the applicability and efficiency of the proposed approach. Results illustrate that the proposed OLVFM-MOGA is able to obtain comparable convergence and diversity of the Pareto frontier as to that obtained by MOGA with HF model, while at the same time significantly reducing the computational cost. (C) 2018 Elsevier B.V. All rights reserved.

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