Teaching Learning Based Optimization Algorithm (TLBO)
TLBO is a metaheuristic optimization method proposed by Rao et al
[21]. This optimization algorithm does not require any
algorithm-specific parameters, except population size and maximum number
of iterations. Like other population-based algorithms, TLBO starts with
a randomly generated population of candidate solutions. Then, the
process of TLBO is divided into two parts namely: the ‘Teacher Phase’
and the ‘Learner Phase’. In the ‘Teacher Phase’ a teacher improves the
mean level of learners. The knowledge of a class increases depending
upon a good teacher because he/she brings the level of his/her learners
to his/her level of knowledge. However, in actual life this is not
always the case because the level of learners depends on other factors
like their aptitudes and their efforts and commitment to learn. Thus, a
teacher can only increase the mean level of his/her learners. In the
‘Learner Phase’ the learners improve their knowledge by interacting with
other learners i.e. between themselves. A learner i interacts
with another learner j randomly selected. A learner learns
something new i.e. increases his knowledge if the second learner has
more knowledge than him. Detailed explanations of TLBO are given in
[21,22].