Solution of optimal power flow using non dominated sorting multi objective opposition based gravitational search algorithm

In this paper, non dominated sorting multi objective opposition based gravitational search algorithm (NSMOOGSA) has been proposed to solve different single and multi objective optimal power flow (OPF) problems. Oppositional learning concept has been used to improve the current population toward some optimal solutions and also to accelerate the convergence of the solutions. The non dominated sorting with crowding distance algorithm has been used to locate and manage the Pareto optimal front. The effectiveness of the proposed method has been tested on standard IEEE 30-bus power system with different conflicting objectives that reflect minimization of fuel cost, active power loss, total emission, bus voltage deviation and improvement of voltage stability. The proposed algorithm has been compared to various non-linear optimization techniques reported in the literature recently. The obtained result shows the potential of the proposed method in achieving the optimal solution. (C) 2014 Elsevier Ltd. All rights reserved.