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Teaching–learning-based optimization with differential and repulsion learning for global optimization and nonlinear modeling

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Abstract

Teaching–learning-based optimization (TLBO) algorithm is one of the recently proposed optimization algorithms. It has been successfully used for solving optimization problems in continuous spaces. To improve the optimization performance of the TLBO algorithm, a modified TLBO algorithm with differential and repulsion learning (DRLTLBO) is presented in this paper. In the proposed algorithm, the differential evolution (DE) operators are introduced into the teacher phase of DRLTLBO to increase the diversity of the new population. In the learner phase of DRLLBO, local learning method or repulsion learning method are adopted according to a certain probability to make learners search knowledge from different directions. In the local learning method, learners learn knowledge not only from the best learner but also from another random learner of their neighbors. In the repulsion learning method, learners learn knowledge from the best learner and keep away from the worst learner of their neighbors. Moreover, self-learning method is adopted to improve the exploitation ability of learners when they are not changed in some continuous generations. To decrease the blindness of random self-learning method, the history information of the corresponding learners in some continuous generations is used in self-learning phase. Furthermore, all learners are regrouped after a certain iterations to improve the local diversity of the learners. In the end, DRLTLBO is tested on 32 benchmark functions with different characteristics and two typical nonlinear modeling problems, and the comparison results show that the proposed DRLTLBO algorithm has shown interesting outcomes in some aspects.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61572224, 41475017 and 11504121) and the National Science Fund for Distinguished Young Scholars (Grants No. 61425009). This work is also partially supported by Anhui Provincial Natural Science Foundation (Grant No. 1708085MF140), the Major Project of Natural Science Research in Anhui Province (Grant No. KJ2015ZD36) and the Natural Science Foundation in colleges and universities of Anhui Province (Grant No. KJ2016A639). The codes of DA is taken from http://www.alimirjalili.com/DA.html.

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Authors and Affiliations

  1. School of Physics and Electronic Information, Huaibei Normal University, Huaibei, China

    Feng Zou, Debao Chen, Renquan Lu, Suwen Li & Lehui Wu

  2. School of Automation, Guangdong University of Technology, Hangzhou, China

    Renquan Lu

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  1. Feng Zou
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  2. Debao Chen
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Correspondence to Feng Zou.

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Communicated by V. Loia.

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Zou, F., Chen, D., Lu, R. et al. Teaching–learning-based optimization with differential and repulsion learning for global optimization and nonlinear modeling. Soft Comput 22, 7177–7205 (2018). https://doi.org/10.1007/s00500-017-2722-4

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