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Pareto-Based Bi-indicator Infill Sampling Criterion for Expensive Multiobjective Optimization

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12654)

Abstract

Infill sampling criteria play a crucial role in saving expensive evaluations for surrogate-assisted multiobjective evolutionary algorithms. Promoting convergence and maintaining diversity in the population are the two main goals of designing a new infilling sampling criterion, which is naturally a bi-objective optimization problem. In this paper, a Pareto-based bi-indicator infill sampling criterion is proposed to select candidate solutions which will be re-evaluated using expensive objective functions. The proposed criterion is embedded into a Gaussian process assisted evolutionary algorithm to solve expensive multiobjective optimization problems. In the proposed criterion, we introduce two indicators measuring the convergence and diversity as two optimization objectives. Empirical studies on the UF and DTLZ problems demonstrate that the proposed algorithm is more effective than other state-of-the-art surrogate-assisted evolutionary algorithms using the same number of expensive function evaluations.

Keywords

  • Gaussian process
  • Expensive multiobjective optimization
  • Infill sampling criterion

Supported by the National Natural Science Foundation of China (No. 61976165).

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Correspondence to Handing Wang .

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Song, Z., Wang, H., Xu, H. (2021). Pareto-Based Bi-indicator Infill Sampling Criterion for Expensive Multiobjective Optimization. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_42

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

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