An improved elitism based teaching-learning optimization algorithm

Teaching-Learning-Based Optimization (TLBO) algorithms simulate the teaching-learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. In this paper, the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. Performance of the improved TLBO algorithm is assessed by implementing it on a range of standard unconstrained benchmark functions having different characteristics. The results of optimization obtained using the improved elitism based TLBO algorithm are validated by comparing them with those obtained using the basic TLBOalgorithms.