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Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application

Author

Listed:
  • Yindi Ma

    (School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China)

  • Yanhai Li

    (School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China)

  • Longquan Yong

    (School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China)

Abstract

This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization problems. Firstly, the perturbation conditions in the “teaching” and “learning” stages of the original TLBO algorithm are interpreted geometrically, based on which the search capability of the TLBO is enhanced by adjusting the range of values of random numbers. Second, a strategic restructuring has been ingeniously implemented, dividing the algorithm into three distinct phases: pre-course self-study, classroom blended learning, and post-course consolidation; this structural reorganization and the random crossover strategy in the self-learning phase effectively enhance the global optimization capability of TLBO. To evaluate its performance, the BLTLBO algorithm was tested alongside seven distinguished variants of the TLBO algorithm on thirteen multimodal functions from the CEC2014 suite. Furthermore, two excellent high-dimensional optimization algorithms were added to the comparison algorithm and tested in high-dimensional mode on five scalable multimodal functions from the CEC2008 suite. The empirical results illustrate the BLTLBO algorithm’s superior efficacy in handling high-dimensional multimodal challenges. Finally, a high-dimensional portfolio optimization problem was successfully addressed using the BLTLBO algorithm, thereby validating the practicality and effectiveness of the proposed method.

Suggested Citation

  • Yindi Ma & Yanhai Li & Longquan Yong, 2024. "Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application," Mathematics, MDPI, vol. 12(10), pages 1-19, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1596-:d:1398063
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    References listed on IDEAS

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    1. Xing, Aosheng & Chen, Yong & Suo, Jinyi & Zhang, Jie, 2024. "Improving teaching-learning-based optimization algorithm with golden-sine and multi-population for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 221(C), pages 94-134.
    2. ShouHeng Tuo, 2016. "A Modified Harmony Search Algorithm For Portfolio Optimization Problems," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 311-326.
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