Paper Info
Title
Improvement In Learning Enthusiasm-Based Tlbo Algorithm With Enhanced Exploration And Exploitation Properties
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.
Year
DOI
Venue
2021
10.1007/s11047-020-09811-5
NATURAL COMPUTING
Keywords
DocType
Volume
Evolutionary algorithms (EAs), TLBO, Learning enthusiasm-based TLBO (LebTLBO), Local Neighborhood Search (LNS), Opposition Based Learning strategy (OBL), WSN
Journal
20
Issue
ISSN
Citations 
3
1567-7818
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Nitin Mittal17710.11
Arpan Garg200.34
Prabhjot Singh351.72
Simrandeep Singh412.04
Harbinder Singh501.01