In the arena of power system operation and planning, the optimal reactive power flow (ORPF) plays a pivotal role, wherein the application of classical techniques poses issues in obtaining the optimal solutions and hence is usually employed with the meta-heuristic and/or bio-inspired techniques, with a view to converge swiftly towards an optimal solution. Usually, ORPF can have uneven, intermittent objectives and multi-constraint functions; and such intricacies of ORPF can best be suppressed by employing a combination of nature-inspired algorithms as a process of hybridization. Thus, in this paper, an approach has been endeavoured to hybridize the Biogeography based optimization (BBO) with that of the predator-prey optimization (PPO), so as to be rightfully termed as "adaptive biogeography based predator-prey optimization" (ABPPO). In such a way, this paper elucidates a novel hybrid technique that includes adaptive mutation combined with predator-prey pattern for attaining the global optimal point. In adaptive mutation scheme, the diversity measure of distance-to-average point is the predominant feature that dodges the supremacy of extremely feasible solutions throughout enhancing the population diversity. The predators explore around the elite prey in a determined way, whereas the preys search the solution space so as to evade from the predators. This tool improves the utilization and searching abilities of the BBO exploration procedure, thereby offers a mean of evading from the suboptimal point and imposes the populace to attain at the global best point. The efficacy of this hybrid scheme is validated against the standard test cases of IEEE-30 and IEEE-57 bus systems. The results show the efficiency and vitality of the proposed method. (C) 2016 Karabuk University. Publishing services by Elsevier B.V.