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An improved TLBO with logarithmic spiral and triangular mutation for global optimization

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Abstract

The teaching–learning-based optimization (TLBO) algorithm is a new optimization technique that has been successfully applied in various optimization fields. However, the TLBO still has a slow convergence rate and difficulty exiting local optima. To overcome these shortcomings, a TLBO algorithm with a logarithmic spiral strategy and a triangular mutation rule (LNTLBO) is introduced. In the teacher phase, a logarithmic spiral strategy that enables students to approach the teacher is incorporated into the original search method to accelerate convergence speed. Meanwhile, a new learning mechanism with a triangular mutation is used to further enhance the abilities of exploration and exploitation in the learner phase. Thirteen unconstrained benchmarks and two constrained optimization problems are employed to examine the LNTLBO. The simulation results prove that the LNTLBO is efficient and useful for global optimization.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61601505.

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Authors and Affiliations

  1. Aeronautics Engineering College, Air Force Engineering University, Xi’an, 710038, China

    Zhuoran Zhang, Changqiang Huang & Bo Han

  2. Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, 710072, China

    Hanqiao Huang

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  1. Zhuoran Zhang
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  2. Hanqiao Huang
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  3. Changqiang Huang
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  4. Bo Han
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Correspondence to Hanqiao Huang.

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Zhang, Z., Huang, H., Huang, C. et al. An improved TLBO with logarithmic spiral and triangular mutation for global optimization. Neural Comput & Applic 31, 4435–4450 (2019). https://doi.org/10.1007/s00521-018-3785-6

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