Solution of optimal power flow using nondominated sorting multi objective gravitational search algorithm

This article presents application of nondominated sorting multi objective gravitational search algorithm (NSMOGSA) for solution of different optimal power flow (OPF) problems. In NSMOGSA, the gravitational acceleration of the original gravitational search algorithm (GSA) is updated using a nondominated sorting concept. Fast elitist nondominated sorting and crowding distance have been used to locate and manage the Pareto optimal front. An external archive of the Pareto optimal solutions are also used to provide some elitism. The proposed method is employed for optimal adjustments of the power system control variables which involve continuous variables of the OPF problem. The efficacy of NSMOGSA has been tested on IEEE 30-bus system with four different objectives that reflect minimization of active power loss, total fuel cost, bus voltage deviation and enhancement of voltage stability. Simulation results are also compared with the results found by other techniques reported in the recent literature to show the algorithmic efficacy of NSMOGSA. The obtained results using NSMOGSA has also been tested on some standard benchmark functions to evaluate its potential. Numerical results reveal the tangible superiority of the proposed method in achieving the optimum solution. (C) 2014 Elsevier Ltd. All rights reserved.