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Growing Science » International Journal of Industrial Engineering Computations » Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 4 Issue 1 pp. 29-50 , 2013

Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems Pages 29-50 Right click to download the paper Download PDF

Authors: R. Venkata Rao, Vivek Patel

DOI: 10.5267/j.ijiec.2012.09.001

Keywords: Number of generations, Population size, Elitism, Teaching-learning-based optimization, Unconstrained optimization problems

Abstract: Teaching-Learning-based optimization (TLBO) is a recently proposed population based algorithm, which simulates the teaching-learning process of the class room. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. In this paper, the effect of elitism on the performance of the TLBO algorithm is investigated while solving unconstrained benchmark problems. The effects of common control parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. A statistical test is also performed to investigate the results obtained using different algorithms. The results have proved the effectiveness of the proposed elitist TLBO algorithm.

How to cite this paper
Rao, R & Patel, V. (2013). Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems.International Journal of Industrial Engineering Computations , 4(1), 29-50.

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Journal: International Journal of Industrial Engineering Computations | Year: 2013 | Volume: 4 | Issue: 1 | Views: 4318 | Reviews: 0

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