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a b s t r a c tGravitational search algorithm (GSA) is based on law of gravity and the interaction between masses. In GSA, searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. In this paper, to further improve the optimization performance of GSA, opposition-based learning is employed in opposition-based gravitational search algorithm (OGSA) for population initialization and also for generation jumping. In the present work, OGSA is applied for the solution of optimal reactive power dispatch (ORPD) of power systems. Traditionally, ORPD is defined as the minimization of active power transmission losses by controlling a number of control variables. ORPD is formulated as a non-linear constrained optimization problem with continuous and discrete variables. In this work, OGSA is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation to optimize certain objectives. The study is implemented on IEEE 30-, 57-and 118-bus test power systems with different objectives that reflect minimization of either active power loss or that of total voltage deviation or improvement of voltage stability index. The obtained results are compared to those yielded by the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature including basic GSA. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving ORPD problems of power systems.