Large-scale bound constrained optimization based on hybrid teaching learning optimization algorithm

Mashwani W.K., Shah H., Kaur M., Bakar M.A., Miftahuddin M.

Instituite of Numerical Sciences, Kohat University of Science & Technology, Pakistan; College of Computer Science, King Khalid University, Abha, Saudi Arabia; School of Engineering and Applied Sciences, Bennett University, Greater Noida, India; Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Malaysia; Faculty of Mathematics and Natural Sciences, Syiah Kuala University, Banda Aceh, Indonesia


Evolutionary computing is an exciting sub-field of soft computing. Many evolutionary algorithm based on the Darwinian principles of natural selection are developed under the umbrella of EC in the last two decades. EAs provide a set of optimal solutions in single simulation unlike traditional optimization techniques for dealing with large-scale global optimization and search problems. Teaching Learning based Optimization (TLBO) is one of the most recently developed EA. TLBO employs a group of learners or a class of learners to perform global optimization search process. The framework of the TLBO consists of two phases, including the Teacher Phase and Learner Phase. The Teacher Phase’ means learning from the teachers and the Learner Phase means learning through interaction among learners. In this paper, we have developed a hybrid TLBO (HTLBO) with aim at to further improve the exploration and exploitation abilities of the baseline TLBO algorithm. The performance of the proposed HTLBO algorithm examined upon using recently designed benchmark functions for the special session of the CEC2017 problems. The experimental results of the proposed algorithm are better than some well-known evolutionary algorithms in terms of proximity and diversity. © 2021 THE AUTHORS

Evolutionary algorithms; Evolutionary computing; Global optimization; Hybrid evolutionary algorithms; Soft computing


Alexandria Engineering Journal

Publisher: Elsevier B.V.

Volume 60, Issue 6, Art No , Page 6013 – 6033, Page Count

Journal Link:

doi: 10.1016/j.aej.2021.04.002

Issn: 11100168

Type: All Open Access, Gold


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