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Improving the Efficiency of R2HCA-EMOA

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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

R2HCA-EMOA is a recently proposed hypervolume-based evolutionary multi-objective optimization (EMO) algorithm. It uses an R2 indicator variant to approximate the hypervolume contribution of each solution. Meanwhile, it uses a utility tensor structure to facilitate the calculation of the R2 indicator variant. This makes it very efficient for solving many-objective optimization problems. Compared with HypE, another hypervolume-based EMO algorithm designed for many-objective problems, R2HCA-EMOA runs faster and at the same time achieves better performance. Thus, R2HCA-EMOA is more attractive for practical use. In this paper, we further improve the efficiency of R2HCA-EMOA without sacrificing its performance. We propose two strategies for the efficiency improvement. One is to simplify the environmental selection, and the other is to change the number of direction vectors depending on the state of evolution. Numerical experiments clearly show that the efficiency of R2HCA-EMOA is significantly improved without deteriorating its performance.

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Notes

  1. 1.

    In this paper, the solutions (individuals) are assumed to be points in the objective space, i.e., a solution \(\mathbf {s}\) denotes an objective vector.

  2. 2.

    rand(0, 1) means a random number between 0 and 1.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 62002152, 61876075), Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Ke Shang, Hisao Ishibuchi, Longcan Chen, Weiyu Chen & Lie Meng Pang

Authors
  1. Ke Shang
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  2. Hisao Ishibuchi
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  3. Longcan Chen
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  4. Weiyu Chen
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  5. Lie Meng Pang
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Corresponding author

Correspondence to Hisao Ishibuchi .

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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Shang, K., Ishibuchi, H., Chen, L., Chen, W., Pang, L.M. (2021). Improving the Efficiency of R2HCA-EMOA. 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_10

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_10

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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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