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An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-objective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2021)

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

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Abstract

Constrained multi-objective optimization problems (CMOPs) are ubiquitous in real-world engineering optimization scenarios. A key issue in constrained multi-objective optimization is to strike a balance among convergence, diversity and feasibility. A recently proposed two-archive evolutionary algorithm for constrained multi-objective optimization (C-TAEA) has be shown as a latest algorithm. However, due to its simple implementation of the collaboration mechanism between its two co-evolving archives, C-TAEA is struggling when solving problems whose pseudo Pareto-optimal front, which does not take constraints into consideration, dominates the feasible Pareto-optimal front. In this paper, we propose an improved version C-TAEA, dubbed C-TAEA-II, featuring an improved update mechanism of two co-evolving archives and an adaptive mating selection mechanism to promote a better collaboration between co-evolving archives. Empirical results demonstrate the competitiveness of the proposed C-TAEA-II in comparison with five representative constrained evolutionary multi-objective optimization algorithms.

This work was supported by UKRI Future Leaders Fellowship (Grant No. MR/S017062/1).

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Notes

  1. 1.

    Complete results are given in the supplementary document of this paper https://tinyurl.com/yy8jw9bo.

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Authors and Affiliations

  1. College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China

    Xinyu Shan

  2. Department of Computer Science, University of Exeter, Exeter, EX4 4QF, UK

    Ke Li

Authors
  1. Xinyu Shan
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  2. Ke Li
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Corresponding author

Correspondence to Ke Li .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

  3. Southern University of Science and Technology, Shenzhen, China

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Shan, X., Li, K. (2021). An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-objective 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_19

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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