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Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence

Author

Listed:
  • Wendi Xu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China
    Institute of Industrial Artificial Intelligence and Optimization, Northeastern University, Shenyang 110819, China)

  • Xianpeng Wang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China
    These authors contributed equally to this work.)

  • Qingxin Guo

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China
    These authors contributed equally to this work.)

  • Xiangman Song

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China
    These authors contributed equally to this work.)

  • Ren Zhao

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China
    These authors contributed equally to this work.)

  • Guodong Zhao

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China)

  • Dakuo He

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China
    Institute of Industrial Artificial Intelligence and Optimization, Northeastern University, Shenyang 110819, China)

  • Te Xu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China)

  • Ming Zhang

    (Key Laboratory for Radio Astronomy, Chinese Academy of Sciences, Nanjing 210000, China
    University of Chinese Academy of Sciences, Beijing 100000, China)

  • Yang Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China
    Frontier Sciences Center for Industrial Intelligence and Systems Optimization, Ministry of Education, Shenyang 110819, China)

Abstract

As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a “decomposition” style (in the abstract of the well-known “Transformer” article “Attention is All You Need”, they use “attention” instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or “gathered” for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two “where to go” strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation.

Suggested Citation

  • Wendi Xu & Xianpeng Wang & Qingxin Guo & Xiangman Song & Ren Zhao & Guodong Zhao & Dakuo He & Te Xu & Ming Zhang & Yang Yang, 2023. "Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence," Mathematics, MDPI, vol. 11(20), pages 1-11, October.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4390-:d:1265043
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    References listed on IDEAS

    as
    1. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    Full references (including those not matched with items on IDEAS)

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