Research Article
A Study from the Perspective of Nature-Inspired Metaheuristic Optimization Algorithms
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 113 - Issue 9 |
| Published: March 2015 |
| Authors: Deepthi S, Aswathy Ravikumar |
10.5120/19858-1810
|
Deepthi S, Aswathy Ravikumar . A Study from the Perspective of Nature-Inspired Metaheuristic Optimization Algorithms. International Journal of Computer Applications. 113, 9 (March 2015), 53-56. DOI=10.5120/19858-1810
@article{ 10.5120/19858-1810,
author = { Deepthi S,Aswathy Ravikumar },
title = { A Study from the Perspective of Nature-Inspired Metaheuristic Optimization Algorithms },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 113 },
number = { 9 },
pages = { 53-56 },
doi = { 10.5120/19858-1810 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Deepthi S
%A Aswathy Ravikumar
%T A Study from the Perspective of Nature-Inspired Metaheuristic Optimization Algorithms%T
%J International Journal of Computer Applications
%V 113
%N 9
%P 53-56
%R 10.5120/19858-1810
%I Foundation of Computer Science (FCS), NY, USA
Abstract
There are various metaheuristic algorithms which can be used to solve optimization problems efficiently. Among these algorithms, nature-inspired optimization algorithms are attractive because of their better results. In this paper, four types of metaheuristic algorithms such as ant colony optimization algorithm, firefly algorithm, bat algorithm and cuckoo search algorithms were used as the basis for comparison. Ant colony optimization algorithm is based on the interactions between social insect, ants. Firefly algorithm is influenced by the flashing behavior of swarming firefly. Cuckoo search uses brooding parasitism of cuckoo species and bat algorithm is inspired by the echolocation of foraging bats.
References
- M. Dorigo and G. Di Caro, "New ideas in optimization", 1999.
- Tang Rui, Simon Fong, Xin-She Yang and Suash Deb, "Nature-inspired Clustering Algorithms for Web Intelligence Data," pp. 978-0-7695-4880-7 vol. 12, 2012.
- Sankalp Arora, Satvir Singh, "A Conceptual Comparison of Firefly Algorithm,Bat Algorithm and Cuckoo Search,"pp. 978-1-4799-1375, 2013.
- Kuang Xiangling and Huang Guangqiu," An Optimization Algorithm Based on Ant Colony Algorithm," 2012.
- Iztok Fister Jr. , Xin-She Yang, Janez Brest and Dusan Fister," A Brief Review of Nature-Inspired Algorithms for Optimization," Elektrotehniski vestnik 80(3):1-7,English Edition, 2013.
- Rui Tang, Simon Fong, Xin-She Yang, Suash Deb," Integrating Nature-inspired Optimization Algorithms to K-means Clustering," IEEE Trans. ,pp. 978-1-4673-2430, 2012.
- X. -S. Yang,"Nature-inspired metaheuristic algorithms", Luniver Press, 2011.
- Z. W. Geem, J. H. Kim, and G. Loganathan, "A new heuristic optimization algorithm: harmony search," Simulation, vol. 76, no. 2, pp. 60–68, 2001.
- A. Kaveh and S. Talatahari, "A novel heuristic optimization method: charged system search," Acta Mechanica, vol. 213, no. 3-4, pp. 267–289, 2010.
- Homayun Afrabandpey, Meysam Ghaffari, Abdolreza Mirzaei, Mehran Safayani,"A Novel Bat Algorithm Based on Chaos for Optimization Tasks,"pp. 4799-3351, 2014.
- Kunja Bihari Swain, S. S Swalanki and Asim Kumar mahakula, "Bio Inspired Cuckoo Search Algorithm Based Neural Network and its Application to Noise Cancellation," pp. 4799-2866, 2014.
- Siddharth Agarwal, Amrit Pal Singh, Nitin Anand, "Evaluation performance study of firefly algorithm, particle-swarm optimization and artificial bee colony algorithm for non-linear mathematical optimization function," ICCCNT, 2013.
- Roberto A. Vazquez, "Training Spiking Neural Models using Cuckoo Search Algorithm," pp. 978-1-4244-7835-4, 2011.
Index Terms
Keywords