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Using a Genetic Algorithm-Based Hyper-Heuristic to Tune MOEA/D for a Set of Benchmark Test Problems

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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

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is one of the most popular algorithms in the EMO community. In the last decade, the high performance of MOEA/D has been reported in many studies. In general, MOEA/D needs a different implementation for a different type of problems with respect to its components such as a scalarizing function, a neighborhood structure, a normalization mechanism and genetic operators. For MOEA/D users who do not have the in-depth knowledge about the algorithm, it is not easy to implement an appropriate algorithm that is suitable for their problems at hand. In our previous studies, we have suggested an offline genetic algorithm-based hyper-heuristic method to tune MOEA/D for a single problem. However, in real-world situations, users may want to use an algorithm with robust performance over many problems. In this paper, we improve the offline genetic algorithm-based hyper-heuristic method for tuning a set of problems. The offline hyper-heuristic procedure is applied to 26 benchmark test problems. The obtained MOEA/D implementations are compared with six decomposition-based EMO algorithms. The experimental results show that the tuned MOEA/D outperformed the compared algorithms on many test problems. The tuned MOEA/D also shows good (and stable) performance over a set of test problems.

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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 Enterpreneurial 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

    Lie Meng Pang, Hisao Ishibuchi & Ke Shang

Authors
  1. Lie Meng Pang
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  2. Hisao Ishibuchi
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  3. Ke Shang
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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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Pang, L.M., Ishibuchi, H., Shang, K. (2021). Using a Genetic Algorithm-Based Hyper-Heuristic to Tune MOEA/D for a Set of Benchmark Test Problems. 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_14

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_14

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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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