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Teaching-Learning-Feedback-Based Optimization

  • Xiang Li
  • Kang LiEmail author
  • Zhile Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)

Abstract

Teaching-learning-based Optimization (TLBO) is a popular meta-heuristic optimisation method that has been used in solving a number of scientific and engineering problems. In this paper, a new variant, namely Teaching-learning-feedback-based Optimization (TLFBO) is proposed. In addition to the two phases in the canonical TLBO, an additional feedback learning phase is employed to further speed up the convergence. The teacher in the previous generation is recorded and communicates with the current teacher to provide combined feedbacks to the learners and supervise the learning direction to avoid wasting computational efforts incurred in the previous generations. Numerical experiments on 10 well-known benchmark functions are conducted to evaluate the performance of the TLFBO, and experimental results show that the proposed TLFBO has a superior and competitive capability in solving continuous optimisation problems.

Keywords

Teaching-learning-based Optimization (TLBO) Feedback Global optimization Heuristic method 

Notes

Acknowledgment

This paper was partially funded by the EPSRC under grant EP/P004636/1 and partially supported by NSFC under 61673256, and Shanghai Science Technology Commission under grant No. 14ZR1414800.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electronics, Electrical Engineering and Computer ScienceQueen’s University BelfastBelfastUK

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