Application of Multi-Objective Teaching Learning Based Optimization Algorithm to Optimal Power Flow Problem


This paper presents a non-domination based sorting multiobjective teaching-learning-based optimization algorithm, for solving the optimal power flow (OPF) problem. The OPF problem is a nonlinear constrained multi-objective optimization problem where the fuel cost, Transmission losses and L-index are to be minimized. Since the problem is treated as a true multi-objective optimization problem, different trade-off solutions are provided. The decision maker has an option to choose a solution among the different trade-off solutions provided in the pareto-optimal front. The standard IEEE 30-bus test system is used and the results show the effectiveness of MOTLBO and confirm its potential to solve the multi-objective OPF problem. Simulation results clearly show that the proposed method is able to produce true and well distributed Pareto optimal solutions for multiobjective OPF problem and the comparison with the results reported in the literature demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective OPF problem