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Anti-Predatory NIA for Unconstrained Mathematical Optimization Problems

Anti-Predatory NIA for Unconstrained Mathematical Optimization Problems
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Author(s): Rohit Kumar Sachan (Motilal Nehru National Institute of Technology Allahabad, Allahabad, India) and Dharmender Singh Kushwaha (Motilal Nehru National Institute of Technology Allahabad, Allahabad, India)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 23
Source title: International Journal of Swarm Intelligence Research (IJSIR)
Editor(s)-in-Chief: Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/IJSIR.2020010101

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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.

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