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

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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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Abbreviations

ABC:

Artificial Bee Colony Algorithm

ACO:

Ant Colony Optimization

AoA:

Angle of Arrival

BA:

Bat Algorithm

BBO:

Biogeography Based Optimization Algorithm

CS:

Cuckoo Search Algorithm

DA:

Dragonfly Algorithm

EAs:

Evolutionary Algorithms

EOBL:

Elite Opposition-Based Learning

FA:

Firefly Algorithm

FPA:

Flower Pollination Algorithm

GA:

Genetic Algorithm

LebTLBO:

Learning enthusiasm-based TLBO

LNS:

Local Neighborhood Search

NBA:

Novel Bat Algorithm

NMRA:

Naked Mole Rat Algorithm

OBL:

Opposition Based Learning strategy

PSO:

Particle Swarm Optimization

RSSI:

Received Signal Strength Indicator

SI:

Swarm Intelligence

SMO:

Spider Monkey Optimization Algorithm

TDoA:

Time Difference of Arrival

TLBO:

Teaching Learning Based Optimization

ToA:

Time of Arrival

WSNs:

Wireless Sensor Networks

WWO:

Water Wave Optimization Algorithm

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, 140413, India

    Nitin Mittal, Arpan Garg, Prabhjot Singh, Simrandeep Singh & Harbinder Singh

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  1. Nitin Mittal
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  2. Arpan Garg
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  3. Prabhjot Singh
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  4. Simrandeep Singh
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  5. Harbinder Singh
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Correspondence to Nitin Mittal.

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Appendix

Appendix

See Table 10.

Table 10 The 100-digit challenge basic test functions (Salgotra and Singh 2019)
Full size table

Algorithm 1.

Pseudo code of TLBO

figure a

Algorithm 2.

Pseudo code of LebTLBO

figure b

Algorithm 3.

Pseudo code of LebTV 1.0

figure c

Algorithm 4.

Pseudo code of LebTV 2.0

figure d

Algorithm 5.

Pseudo code of LebTV 3.0

figure e

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Mittal, N., Garg, A., Singh, P. et al. Improvement in learning enthusiasm-based TLBO algorithm with enhanced exploration and exploitation properties. Nat Comput 20, 577–609 (2021). https://doi.org/10.1007/s11047-020-09811-5

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