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Ensemble Strategy Based Hyper-heuristic Evolutionary Algorithm for Many-Objective Optimization

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 703))

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Abstract

Many-objective Optimization problems (MaOPs), with four or more objectives are difficult to solve, is a kind of common optimization problems in actual industrial production. In recent years, a large number of many-objective optimization algorithms (MaOEAs) have been proposed to solve various types of MaOPs. However, in practical problems, it is usually hard to improve the existing optimization algorithms or make a lot of attempts for MaOEAs because the true Pareto surface is usually unknown in a new MaOPs, which is a time-consuming and uncertain task. In this paper, inspired by the selective hyper heuristic optimization algorithm, we propose an integrated hyper-heuristic many-objective optimization algorithm (MaOEA-EH), which can integrate the existing advanced MaOEAs by simulating the PBFT consensus mechanism in the blockchain, and select the best algorithm for the current problem through the voting-election method in the iterative process. Numerical results show that our algorithm performs well on various many-objective problems.

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Authors and Affiliations

  1. Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China

    Wang Qian, Zhang Jingbo & Cui Zhihua

Authors
  1. Wang Qian
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  2. Zhang Jingbo
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  3. Cui Zhihua
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Corresponding author

Correspondence to Wang Qian .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Qian, W., Jingbo, Z., Zhihua, C. (2024). Ensemble Strategy Based Hyper-heuristic Evolutionary Algorithm for Many-Objective Optimization. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_18

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  • DOI: https://doi.org/10.1007/978-3-031-57808-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57807-6

  • Online ISBN: 978-3-031-57808-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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