DOI: 10.4018/IJSIR.2020010101 International Journal of Swarm Intelligence Research Volume 11 • Issue 1 • January-March 2020 Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 1 Anti-Predatory NIA for Unconstrained Mathematical Optimization Problems Rohit Kumar Sachan, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India https://orcid.org/0000-0001-6018-3716 Dharmender Singh Kushwaha, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India ABSTRACT Nature-Inspired Algorithms (NIAs) are one of the most efficient methods to solve the optimization problems. A recently proposed NIA is the anti-predatory NIA, which is based on the anti-predatory behavior of frogs. This algorithm uses five different types of self-defense mechanisms in order to improve its anti-predatory strength. This paper demonstrates the computation steps of anti-predatory for solving the Rastrigin function and attempts to solve 20 unconstrained minimization problems using anti-predatory NIA. The performance of anti-predatory NIA is compared with the six competing meta- heuristic algorithms. A comparative study reveals that the anti-predatory NIA is a more promising than the other algorithms. To quantify the performance comparison between the algorithms, Friedman rank test and Holm-Sidak test are used as statistical analysis methods. Anti-predatory NIA ranks first in both cases of “Mean Result” and “Standard Deviation.” Result measures the robustness and correctness of the anti-predatory NIA. This signifies the worth of anti-predatory NIA in the domain of mathematical optimization. KeywORdS Anti-Predatory NIA, Evolutionary, Meta-Heuristic, Nature-Inspired Algorithms, Optimization, Swarm Intelligence INTROdUCTION Over the last decade, Nature-Inspired Algorithms (NIAs) have become surprisingly very popular for solving the mathematical optimization problems and even for various real-world optimization problems. This is because NIAs are highly flexible, efficient to solve optimization problems and do not trap in locally optimal solutions. These algorithms are so named, because the optimization methods adapt from natural phenomena (Fister Jr. et al., 2013). The natural phenomena always solve the problem in an optimal way. Based on that optimal way, NIAs try to solve optimization problems. A large number of NIAs are proposed till now, but no NIA gives a superior performance in all optimization problems (Wolpert and Macready, 1997). Hence, there is still a need of an efficient and robust NIA. Based on the nature of objective function, optimization problems are classified as: maximization and minimization problems (Yang, 2013). These can also be constrained or unconstrained (Yang, 2013). Constrained problems impose restrictions on the parameters whereas unconstrained problems don’t