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KMCGO: Kriging-Assisted Multi-objective Constrained Global Optimization

  • Yaohui Li
  • Yizhong Wu
  • Yuanmin ZhangEmail author
  • Shuting Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)

Abstract

The Kriging method based single-objective optimization have been preventing the application of some engineering design problems. The main challenge is how to explore a method that can improve convergence accuracy and reduce time cost under the conditions of parallel simulation estimation. For this purpose, a Kriging-assisted multi-objective constrained global optimization (KMCGO) algorithm is proposed. In KMCGO, Kriging models of expensive objective and constraint functions are firstly constructed or updated with the sampled data. And then, the objective, root mean square error and feasibility probability, which will be predicted by Kriging models, are used to construct three optimization objectives. After optimizing the three objectives by the NSGA-II solver, the new sampling points produced by the Pareto optimal solutions will be further screened to obtain better design points. Finally, four numerical tests and a design problem are checked to illustrate the feasibility, stability and effectiveness of the proposed method

Keywords

Constrained global optimization Surrogate models Kriging Infill search criterion Multi-objective optimization 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 51775472, No. 51675197, No. 51575205).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yaohui Li
    • 1
  • Yizhong Wu
    • 2
  • Yuanmin Zhang
    • 1
    Email author
  • Shuting Wang
    • 2
  1. 1.Xuchang UniversityXuchangChina
  2. 2.Huazhong University of Science and TechnologyWuhanChina

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