Anti-Predatory NIA for Unconstrained Mathematical Optimization Problems

Anti-Predatory NIA for Unconstrained Mathematical Optimization Problems

Rohit Kumar Sachan, Dharmender Singh Kushwaha
Copyright: © 2020 |Pages: 23
DOI: 10.4018/IJSIR.2020010101
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

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.
Article Preview
Top

Review Of Nias

Most NIAs are driven by the social behavior of the species in the nature which depends on their biological necessities (Bragg, 1945). All species have three main biological necessities in life. These are food and feed; protection from enemies and the environment; and breeding (Bragg, 1945). These biological necessities form the basis of most NIAs proposed by researchers. Some of them are discussed here in terms of inspiration behind them.

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024)
Volume 14: 3 Issues (2023)
Volume 13: 4 Issues (2022)
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing