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A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization

Author

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  • Zhiwei Feng
  • Qingbin Zhang
  • Qingfu Zhang
  • Qiangang Tang
  • Tao Yang
  • Yang Ma

Abstract

In many engineering optimization problems, objective function evaluations can be extremely computationally expensive. The effective global optimization (EGO) is a widely used approach for expensive optimization. Balance between global exploration and local exploitation is a very important issue in designing EGO-like algorithms. This paper proposes a multiobjective optimization based EGO (EGO-MO) for addressing this issue. In EGO-MO, a global surrogate model for the objective function is firstly constructed using some initial database of designs. Then, a multiobjective optimization problem (MOP) is formulated, in which two objectives measure the global exploration and local exploitation. At each generation, the multiobjective evolutionary algorithm based on decomposition is used for solving the MOP. Several solutions selected from the obtained Pareto front are evaluated. In such a way, it can generate multiple test solutions simultaneously to take the advantage of parallel computing and reduce the computational time. Numerical experiments on a suite of test problems have shown that EGO-MO outperforms EGO in terms of iteration numbers. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Zhiwei Feng & Qingbin Zhang & Qingfu Zhang & Qiangang Tang & Tao Yang & Yang Ma, 2015. "A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization," Journal of Global Optimization, Springer, vol. 61(4), pages 677-694, April.
    • Handle: RePEc:spr:jglopt:v:61:y:2015:i:4:p:677-694
    DOI: 10.1007/s10898-014-0210-2
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    Citations

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    Cited by:

    1. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    2. Li, Mingyang & Tang, Jinjun, 2023. "Simulation-based optimization considering energy consumption for assisted station locations to enhance flex-route transit," Energy, Elsevier, vol. 277(C).
    3. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Balancing global and local search in parallel efficient global optimization algorithms," Journal of Global Optimization, Springer, vol. 67(4), pages 873-892, April.
    4. Xiaodong Song & Mingyang Li & Zhitao Li & Fang Liu, 2021. "Global Optimization Algorithm Based on Kriging Using Multi-Point Infill Sampling Criterion and Its Application in Transportation System," Sustainability, MDPI, vol. 13(19), pages 1-17, September.
    5. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    6. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Pseudo expected improvement criterion for parallel EGO algorithm," Journal of Global Optimization, Springer, vol. 68(3), pages 641-662, July.
    7. Zhe Liu & Shurong Li, 2022. "A numerical method for interval multi-objective mixed-integer optimal control problems based on quantum heuristic algorithm," Annals of Operations Research, Springer, vol. 311(2), pages 853-898, April.
    8. Jesús Martínez-Frutos & David Herrero-Pérez, 2016. "Kriging-based infill sampling criterion for constraint handling in multi-objective optimization," Journal of Global Optimization, Springer, vol. 64(1), pages 97-115, January.

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