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Published December 1, 2012 | public
Journal Article

A note on teaching–learning-based optimization algorithm

Abstract

Teaching–Learning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO's dominance. This paper reports our findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. Our findings have revealed three important mistakes regarding TLBO: (1) at least one unreported but important step; (2) incorrect formulae on a number of fitness function evaluations; and (3) misconceptions about parameter-less control. Additionally, unfair experimental settings/conditions were used to conduct experimental comparisons (e.g., different stopping criteria). The experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy. The ultimate goal of this paper is to provide reminders for metaheuristics' researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects, and to allow fair comparisons of the TLBO algorithm to be made with other metaheuristic algorithms.

Additional Information

© 2012 Elsevier Inc. Received 13 October 2011. Received in revised form 10 May 2012. Accepted 16 May 2012. Available online 28 May 2012. The third author's work was partly sponsored by the Slovene Human Resources Development and Scholarship Fund. The authors would also like to thank Marjan Mernik for fruitful discussions and guidelines.

Additional details

Created:
August 22, 2023
Modified:
October 18, 2023