This paper presents, a novel nature-inspired optimization paradigm, named as owl search algorithm (OSA) for solving global optimization problems. The OSA is a population based technique based on the hunting mechanism of the owls in dark. The proposed method is validated on commonly used benchmark problems in the field of optimization. The results obtained by OSA are compared with the results of six state-of-the-art optimization algorithms. Simulation results reveal that OSA provides promising results as compared to the existing optimization algorithms. Moreover, to show the efficacy of the proposed OSA, it is used to design two degree of freedom PI (OSA-2PI) controller for temperature control of a real-time heat flow experiment (HFE). Experimental results demonstrate that OSA-2PI controller is more precise for temperature control of HFE in comparison to the conventional PI controller.
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