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Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

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Abstract

The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks, including the seven unimodal tasks (f1–f7) and six multimodal tasks (f8–f13). The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques, such as HS, PSO, MFO, GA and HHO. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article (and its supplementary information files).

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Acknowledgements

This work was supported by the Philosophy and Social Sciences General Project of Shanghai (No.2022BGL010), the National Natural Science Foundation of China (No.71840003), Key Soft Science Project of Science and Technology Innovation Action Plan of Shanghai Municipal Science and Technology Commission (No. 20692104300), and Science and Technology Development Foundation of University of Shanghai for Science and Technology (No.2018KJFZ043).

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Authors and Affiliations

  1. Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Xuefen Chen, Chunming Ye, Yang Zhang, Lingwei Zhao, Jing Guo & Kun Ma

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  1. Xuefen Chen
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  2. Chunming Ye
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  3. Yang Zhang
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  4. Lingwei Zhao
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Correspondence to Chunming Ye.

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Chen, X., Ye, C., Zhang, Y. et al. Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks. Evol. Intel. 17, 1463–1480 (2024). https://doi.org/10.1007/s12065-023-00839-x

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