Teaching Learning Based Optimization with focused learning and its performance on CEC2017 functions

In this work, we propose a variant to the Teaching Learning Based Optimization algorithm by incorporating focused learning of students. A student undergoes focused learning phase only when it is unable to obtain a better solution in the teacher phase and is expected to efficiently utilize the limited functional evaluations. The performance of this variant is evaluated on the single objective bound constrained real-parameter numerical optimization problems which have been proposed as a part of IEEE Congress on Evolutionary Computation. The proposed variant has provided competitive results in most of the problems.