Improvement in learning enthusiasm-based TLBO algorithm with enhanced exploration and exploitation properties
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Improvement in learning enthusiasm-based TLBO algorithm with enhanced exploration and exploitation properties Nitin Mittal1 • Arpan Garg1 • Prabhjot Singh1 • Simrandeep Singh1 • Harbinder Singh1 Accepted: 25 September 2020 Springer Nature B.V. 2020
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. Keywords Evolutionary algorithms (EAs) TLBO Learning enthusiasm-based TLBO (LebTLBO) Local Neighborhood Search (LNS) Opposition Based Learning strategy (OBL) WSN 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 & Nitin Mittal [email protected] 1
Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab 140413, India
OBL PSO RSSI SI SMO TDoA TLBO ToA WSNs WWO
Opposition Based Learning strategy Particle Swarm Optimization Received Signal Strength Indicator Swarm Intelligence Spider Monkey Optimization Algorithm Time Difference of Arrival Teaching Learning Based Optimization Time of Arrival Wireless Sensor Networks Water Wave Optimization Algorithm
1 Introduction The method of optimization has become an essential component of engineering and business issues. The optimization objectives may be to maximize effectiveness, productivity, performance, or social welfare. Resources, money,
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