Journal of Intelligent & Fuzzy Systems 34 (2018) 1573–1582 DOI:10.3233/JIFS-169452 IOS Press 1573 Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization Mohit Jain a,∗ , Shubham Maurya b , Asha Rani a and Vijander Singh a a Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, University of Delhi, Delhi, India b Department of Electronics and Communication, GLA University, Mathura, Uttar Pradesh, India Abstract. 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. Keywords: Nature-inspired algorithm, unconstrained optimization, two degree of freedom PI controller, Heat flow experiment 1. Introduction In recent years, metaheuristic optimization techniques have gained significant attention of researchers due to successful application of these techniques in a variety of complex optimization problems. These techniques are found more effective than conventional methods which use derivative information of function. Two eminent features of any metaheuristic technique are exploration and exploitation [1]. Exploration phase of algorithm, also known as diversification, redirects the search towards unvisited regions of the search space, in order to find new but potentially better solutions. On the other hand, exploitation or intensification phase ∗ Corresponding author. Mohit Jain, ICE Division, NSIT, Sec- 3, Dwarka, New Delhi, University of Delhi, India. Tel.: +91 9650298558; Fax: +011 25099022; E-mails: mohit.jain@nsit.ac.in and nsit.mohit@gmail.com. helps the algorithm to search in the neighbourhood of current best solutions. There are distinct objectives behind the development of modern metaheuristics such as fast and effortless handling of complex as well as large problems and designing more effective and robust techniques [2]. There is no limitation on the source of motivation to design a metaheuristic technique. As an illus- tration, the gravitational search algorithm (GSA) is inspired from law of gravitation and mass interac- tion [3], interior search algorithm (ISA) is based on the concepts of interior designing and decoration [4] etc. Nevertheless, nature is always a primary source of motivation for proposing new metaheuristic tech- niques. A brief literature review of nature-inspired optimization algorithms is presented in Table 1. Various nature-inspired optimization algorithms are available in literature, however “no free lunch (NFL)” theorem [19] supports the present study as proposed 1064-1246/18/$35.00 © 2018 – IOS Press and the authors. All rights reserved