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Improved teaching–learning-based optimization algorithm with Cauchy mutation and chaotic operators

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Abstract

Teaching–Learning-Based Optimization (TLBO) is a population-based intelligent optimization algorithm, which simulates the "teaching" process of teachers to students and the "learning" process of students in the class. In order to solve the problems of slow optimization speed, low optimization accuracy and easy to fall into local optimization, an improved TLBO algorithm based on Cauchy mutation and chaos operators are proposed. Firstly, the dynamic selection of teachers in the "teaching" stage leads to higher class average grades. Learning from the best students in the class during the "learning" phase makes class results more focused. Secondly, after a teaching is completed, Cauchy mutation is carried out to make the algorithm population more diverse so as to get rid of the local optimal solution. Finally, on the basis of Cauchy mutation, chaos theory is introduced into the optimization process of TLBO algorithm, and 10 chaos are embedded in the process of generating random numbers by Cauchy mutation, which enhances its ergo city and irreconcilability to further improve its convergence speed and accuracy. The performance of the proposed improved TLBO algorithm was tested by using 30 benchmark functions in CEC-BC-2017, and finally two engineering design problems (cantilever arm design and pressure vessel design) were optimized. The experimental results show that the proposed TLBO algorithm has significantly improved its convergence speed and optimization accuracy.

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Acknowledgements

This work was supported by the Basic Scientific Research Project of Institution of Higher Learning of Liaoning Province (Grant No. LJKZ0293), and the Postgraduate Education Reform Project of Liaoning Province (Grant No. LNYJG2022137).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, People’s Republic of China

    Yin-Yin Bao, Cheng Xing, Jie-Sheng Wang, Xiao-Rui Zhao, Xing-Yue Zhang & Yue Zheng

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  1. Yin-Yin Bao
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  2. Cheng Xing
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  3. Jie-Sheng Wang
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  4. Xiao-Rui Zhao
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  5. Xing-Yue Zhang
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Contributions

Yin-Yin Bao participated in the data collection, analysis, algorithm simulation, and draft writing. Cheng Xing and Jie-Sheng Wang participated in the concept, design, interpretation and commented on the manuscript. Xiao-Rui Zhao, Xing-Yue Zhang and Yue Zheng participated in the critical revision of this paper.

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Correspondence to Cheng Xing.

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Bao, YY., Xing, C., Wang, JS. et al. Improved teaching–learning-based optimization algorithm with Cauchy mutation and chaotic operators. Appl Intell 53, 21362–21389 (2023). https://doi.org/10.1007/s10489-023-04705-2

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