5. Teaching-Learning-Based Optimization Algorithm

Teaching-learning-based optimization (TLBO) algorithm was first developed in 2011 by Rao et al. [1] based on the classical school learning process. This algorithm consists of two stages: effect of a teacher on learners and the influence of learners on each other. TLBO initialized with a population of random solutions, named students or learners. The best learner or the smartest student with the best objective function is assigned as the teacher in each iteration of the algorithm. Students are updated iteratively to search the optimum within two phases: based on the knowledge transfer from a teacher first (teacher phase) and then from interaction with other students (learner phase). In TLBO the performance of the class in learning or the performance of teacher in teaching is considered as a normal distribution of marks obtained by the students. The main difference of two normal distributions is in their mean value, i.e., a better teacher teaches students with higher average scores. In this regard, TLBO improves other students in the teacher phase by using the difference between the teacher’s knowledge and the average knowledge of all the students. The knowledge of each student is obtained based on the position taken place by that student in the search space. In a class, students also improve themselves via interacting with each other after the teaching is completed by the teacher. In the learner phase, TLBO improves each student by the knowledge interaction between that student and another randomly selected one.