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 optimal power flow (opf) problems considering effects of valvepoint loading (ve) and prohibited zones (poz). problem formulation includes minimization of cost, loss, voltage stability index (vsi) and 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 are not yet available in the literature. efficacy of sos in resolving opf is recognized in the literature. an opposition based learning technique named quasireflection, is merged into existing sos to enhance its prospects of getting nearer to superior quality solution. the proposed algorithm, named quasireflected 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 the systems by large margins (78.98 % in case of vd when compared to sos and nsgaii 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.