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Generalized Test Suite for Continuous Dynamic 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

Dynamic multi-objective optimization (DMO) has recently attracted increasing interest. Suitable benchmark problems are crucial for evaluating the performance of DMO solvers. However, most of the existing DMO benchmarks mainly focus on Pareto-optimal solutions (PS) varying on the hyperplane, which may produce some unexpected bias for algorithmic analysis. Furthermore, they do not comprehensively consider the general time-linkage property, yet which is commonly observed in real-world applications. To alleviate these two issues, we designed a generalized test suite (GTS) for DMO with the following two advantages over previous existing benchmarks: 1) the PS can change on the hypersurface over time, to better compare the tracking ability of different DMO solvers; 2) the general time-linkage feature is included to systemically investigate the algorithmic robustness in the dynamic environment. Experimental results on five representative DMO algorithms demonstrated the proposed GTS can efficiently discriminate the performance of DMO algorithms and is more general than existing benchmarks.

C. Shao and Q. Zhao—Contribute equally.

The source code of GTS is available at https://dynamicoptimization.github.io.

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Acknowledgement

This work is supported by the National Science Foundation of China under the Grant No. 61761136008, the Science and Technology Innovation Committee Foundation of Shenzhen under the Grant No. JCYJ20200109141235597, the Shenzhen Peacock Plan under the Grant No. KQTD2016112514355531, and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under the Grant No. 2017ZT07X386.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Chang Shao, Qi Zhao & Yuhui Shi

  2. The Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia

    Chang Shao & Jing Jiang

  3. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Qi Zhao

Authors
  1. Chang Shao
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  2. Qi Zhao
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  3. Yuhui Shi
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  4. Jing Jiang
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Corresponding authors

Correspondence to Yuhui Shi or Jing Jiang .

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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Shao, C., Zhao, Q., Shi, Y., Jiang, J. (2021). Generalized Test Suite for Continuous Dynamic 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_17

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_17

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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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