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Published in: Natural Computing 3/2021

10-11-2020

Improvement in learning enthusiasm-based TLBO algorithm with enhanced exploration and exploitation properties

Authors: Nitin Mittal, Arpan Garg, Prabhjot Singh, Simrandeep Singh, Harbinder Singh

Published in: Natural Computing | Issue 3/2021

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Abstract

Learning enthusiasm-based Teaching Learning Based Optimization (LebTLBO) is a metaheuristic inspired by the classroom teaching and learning method of TLBO. In recent years, it has been effectively used in several applications of science and engineering. In the conventional TLBO and most of its versions, all the learners have the same probability of getting knowledge from others. LebTLBO is motivated by the different probabilities of acquiring knowledge by the learner from others and introduced a learning enthusiasm mechanism into the basic TLBO. In this work, to achieve the enhanced performance of conventional LebTLBO by balancing the exploration and exploitation capabilities, an improved LebTLBO algorithm is proposed. The exploration of LebTLBO has been enhanced by the incorporation of the Opposition Based Learning strategy. Exploitation has been improved by Local Neighborhood Search inspired by the experience of the best solution so far discovered in a local neighborhood of the present solution. On the CEC2019 benchmark functions, the suggested technique is assessed, and computational findings show that it provides promising outcomes over other algorithms. Finally, improved LebTLBO is employed in three engineering problems and the competitive findings demonstrate its potential for a real-world problem such as the localization problem in Wireless Sensor Networks.

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Appendix
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Metadata
Title
Improvement in learning enthusiasm-based TLBO algorithm with enhanced exploration and exploitation properties
Authors
Nitin Mittal
Arpan Garg
Prabhjot Singh
Simrandeep Singh
Harbinder Singh
Publication date
10-11-2020
Publisher
Springer Netherlands
Published in
Natural Computing / Issue 3/2021
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-020-09811-5

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