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Empirical study on meta-feature characterization for multi-objective optimization problems

  • S.I.: NCAA 2021
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Algorithm recommendation based on meta-learning was studied previously. The research on the meta-features extraction, which is a key for the success of recommendation, is lacking for multi-objective optimization problems (MOPs). This paper proposes four sets of meta-features to characterize MOPs. In addition, the algorithm recommendation model based on meta-learning is extended to the field of multi-objective optimization. To evaluate the efficiency and effectiveness of the extracted meta-features, 29 MOPs benchmark functions with different dimensions and two real-world MOPs are employed for comprehensive comparison. Experimental results show that the proposed meta-features in this paper can fully characterize MOPs and are empirically efficient for algorithm recommendation.

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Data availability

The MOEAs and MOPs used to support the findings are available from PlatEMO.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 71971142), the Natural Science Foundation of Guangdong Province (No. 2022A1515010278, 2021A1515110595 and 2016A030310067), the Major Research plan of the National Natural Science Foundation of China (No. 91846301), and the Major Project for National Natural Science Foundation of China (No. 71790615).

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, China

    Xianghua Chu, Jiayun Wang & Shuxiang Li

  2. Institute of Big Data Intelligent Management and Decision, Shenzhen University, Shenzhen, China

    Xianghua Chu

  3. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China

    Yujuan Chai

  4. Mount School, York, UK

    Yuqiu Guo

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  1. Xianghua Chu
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Correspondence to Shuxiang Li or Yujuan Chai.

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Chu, X., Wang, J., Li, S. et al. Empirical study on meta-feature characterization for multi-objective optimization problems. Neural Comput & Applic 34, 16255–16273 (2022). https://doi.org/10.1007/s00521-022-07302-5

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