Reactive Power Handling by a Multi-Objective Teaching Learning Optimizer Based on Decomposition

The teaching learning-based optimization (TLBO) is a population-based optimization algorithm suitable for solving complex problems. TLBO imitates the interaction between a teacher and her/his students. The global solution search process of this approach consists of two phases: the teacher- and the learner-phase. This paper proposes a multi-objective teaching learning algorithm based on decomposition (MOTLA/D) for solving a reactive power handling problem. The proposed method is validated on three test systems, and it is compared with respect to a state-of-the-art multi-objective evolutionary algorithm based on decomposition (MOEA/D).