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A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Author

Listed:
  • Honghua Rao

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Heming Jia

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Di Wu

    (School of Education and Music, Sanming University, Sanming 365004, China)

  • Changsheng Wen

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Shanglong Li

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Qingxin Liu

    (School of Computer Science and Technology, Hainan University, Haikou 570228, China)

  • Laith Abualigah

    (Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
    Faculty of Information Technology, Middle East University, Amman 11831, Jordan)

Abstract

The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results.

Suggested Citation

  • Honghua Rao & Heming Jia & Di Wu & Changsheng Wen & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(20), pages 1-36, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3765-:d:940539
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    References listed on IDEAS

    as
    1. Changsheng Wen & Heming Jia & Di Wu & Honghua Rao & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem," Mathematics, MDPI, vol. 10(19), pages 1-36, October.
    2. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah & Yuxiang Liu, 2022. "A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems," Mathematics, MDPI, vol. 10(9), pages 1-30, May.
    3. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 10(7), pages 1-42, March.
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    Citations

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    Cited by:

    1. Jinhua You & Heming Jia & Di Wu & Honghua Rao & Changsheng Wen & Qingxin Liu & Laith Abualigah, 2023. "Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    2. Marcel Nicola & Claudiu-Ionel Nicola, 2022. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning," Mathematics, MDPI, vol. 10(24), pages 1-34, December.
    3. Di Wu & Honghua Rao & Changsheng Wen & Heming Jia & Qingxin Liu & Laith Abualigah, 2022. "Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(22), pages 1-41, November.

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