A novel multi-objective optimizer for handling reactive power

摘要

A novel population-based optimization algorithm for solving a reactive power handling problem is proposed. The algorithm mimics the interaction between the teacher and students. The searching process is broken down in two parts: the Teacher Phase and the Learner Phase. This paper proposes a multi-objective teaching learning algorithm based on decomposition (MOTLA/D). The proposed method is validated on a 190-buses test system, and it is compared with respect to a decomposition-based multi-objective evolutionary algorithm (MOEA/D), which represents a state-of-the-art algorithm.