期刊名称：International Journal of Industrial Engineering Computations
印刷版ISSN：1923-2926
电子版ISSN：1923-2934
出版年度：2013
卷号：4
期号：1
页码：29-50
DOI：10.5267/j.ijiec.2012.09.001
语种：English
出版社：Growing Science Publishing Company
摘要：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.
关键词：Number of generations; Population size; Elitism; Teaching-learning-based optimization; Unconstrained optimization problems
