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].