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Teaching-Learning-Based Optimization State-of-the-Art

Ramachandra Rao Kurada, Karteeka Pavan Kanadam


This paper circumscribes the state-of-the-art to the most recent potential immigrant algorithm in computational intelligence, familiarized as Teaching-Learning-Based Optimization (TLBO). The basic ideology of TLBO was the simulation of schoolroom cognitive process into algorithmic instructions with two essential functions teacher phase and learners phase.  Broadly, TLBO is a population based algorithm, which uses its inhibited solution to extract universal solutions. This nature inspired meta-heuristic is the brainchild of R. V. Rao et al., and presently available in three releases Basic, Elitist and Improved versions. All these algorithms efficiency and effectiveness were experimentally proven in solving engineering optimization problems. The literature on TLBO and its versions, TLBO incorporated clustering approaches practiced by prospective researchers is archived in this survey.


Teaching-Learning-Based Optimization, Meta- heuristics, Evolutionary Algorithms, Multi-Objective Optimization, Clustering.

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