Quasi-reflection-based symbiotic organisms search algorithm for solving static optimal power flow problem

This paper offers a novel variant to the existing Symbiotic Organisms Search (SOS) algorithm to address the Optimal Power Flow (OPF) problems considering effects of valve-point loading (VE) and prohibited zones (POZ). Problem formulation includes minimization of cost, loss, Voltage Stability Index (VSI), Voltage Deviation (VD), and simultaneous minimization of their combinations. Quadratic cost function, effects of VE, and effects of both VE and POZ have been considered. OPF formulation considering effects of both VE and POZ is not yet available in the literature. Efficacy of SOS in resolving OPF is recognized in the literature. An opposition-based learning technique, named quasi-reflection, is merged into existing SOS to enhance its prospects of getting closer to superior quality solution. The proposed algorithm, named Quasi-Reflected Symbiotic Organisms Search (QRSOS), is assessed for IEEE 30 and IEEE 118 bus test systems. It shows promising results in reducing the objective function values of both systems by large margins (78.98% in case of VD when compared to SOS and NSGA-II and 46.06% in case of loss as compared to QOTLBO in IEEE 30 and IEEE 118 bus, respectively). QRSOS also outperformed its predecessors in terms of convergence speed and global search ability. (C) 2019 Sharif University of Technology. All rights reserved.