Improved version of teaching learning-based optimization algorithm using random local search: TLBO-RLS
ISSN: 0332-1649
Article publication date: 3 June 2019
Issue publication date: 3 June 2019
Abstract
Purpose
This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.
Design/methodology/approach
Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS).
Findings
At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work.
Originality value
New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.
Keywords
Citation
Kheireddine, B., Zoubida, B. and Tarik, H. (2019), "Improved version of teaching learning-based optimization algorithm using random local search: TLBO-RLS", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 38 No. 3, pp. 1048-1060. https://doi.org/10.1108/COMPEL-09-2018-0373
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited