4.7 Article

A note on teaching-learning-based optimization algorithm

期刊

INFORMATION SCIENCES
卷 212, 期 -, 页码 79-93

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2012.05.009

关键词

Teaching-learning-based optimization; Constrained optimization problems; Unconstrained optimization problems; Experimental replication

资金

  1. Slovene Human Resources Development and Scholarship Fund

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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. (C) 2012 Elsevier Inc. All rights reserved.

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