An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems

Kunjie Yu (), Xin Wang () and Zhenlei Wang ()
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Kunjie Yu: East China University of Science and Technology
Xin Wang: Shanghai Jiao Tong University
Zhenlei Wang: East China University of Science and Technology

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 4, No 10, 843 pages

Abstract: Abstract The teaching-learning-based optimization (TLBO) algorithm, one of the recently proposed population-based algorithms, simulates the teaching-learning process in the classroom. This study proposes an improved TLBO (ITLBO), in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm. The feedback phase is used to enhance the learning style of the students and to promote the exploration capacity of the TLBO. The mutation crossover operation of DE is introduced to increase population diversity and to prevent premature convergence. The chaotic perturbation mechanism is used to ensure that the algorithm can escape the local optimal. Simulation results based on ten unconstrained benchmark problems and five constrained engineering design problems show that the ITLBO algorithm is better than, or at least comparable to, other state-of-the-art algorithms.

Keywords: Improved teaching-learning-based optimization; Differential evolution; Chaotic perturbation; Unconstrained optimization; Constrained optimization (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s10845-014-0918-3

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