CFP last date
20 January 2025
Reseach Article

Analysis of Chemotaxis in Bacterial Foraging Optimization Algorithm

by Livjeet Kaur, Mohinder Pal Joshi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 4
Year of Publication: 2012
Authors: Livjeet Kaur, Mohinder Pal Joshi
10.5120/6895-9242

Livjeet Kaur, Mohinder Pal Joshi . Analysis of Chemotaxis in Bacterial Foraging Optimization Algorithm. International Journal of Computer Applications. 46, 4 ( May 2012), 18-23. DOI=10.5120/6895-9242

@article{ 10.5120/6895-9242,
author = { Livjeet Kaur, Mohinder Pal Joshi },
title = { Analysis of Chemotaxis in Bacterial Foraging Optimization Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 4 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number4/6895-9242/ },
doi = { 10.5120/6895-9242 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:38:52.310701+05:30
%A Livjeet Kaur
%A Mohinder Pal Joshi
%T Analysis of Chemotaxis in Bacterial Foraging Optimization Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 4
%P 18-23
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For the last few decades, algorithms like Genetic Algorithms, Evolutionary Programming, and Evolutionary Strategies etc. are being used for optimization of various problems. Nowadays various swarm inspired algorithms have replaced them. Bacterial Foraging Optimization (BFO) is the latest among these algorithms. It has been widely accepted as global optimization technique due to its ease of implementation. In this paper we analyzed chemotactic behavior of bacteria by minimizing various mathematical benchmark functions. MATLAB simulations of these functions for different step sizes are shown in graphical form. Work is concluded by discussing the effect of varying step size on chemotactic movement of bacteria.

References
  1. C. A. Floudas, "Deterministic Global Optimization: Theory, Methods and Applications", vol. 37 of Nonconvex Optimization and Its Applications, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000.
  2. J. H. Holland, "Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to B, control, and Artificial Intelligence", University of Michigan Press, Ann Arbor, Michigan, USA, 1975.
  3. M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: Optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics. Part B, vol. 26, no. 1, pp. 29–41, 1996.
  4. Kennedy, J. , & Eberhart, R. C. , "Particle swarm optimization" in Proceedings of IEEE international conference on neural networks, Piscataway, NJ, pp. 1942–1948, (1995).
  5. K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control," IEEE Control Syst. Mag. , vol. 22, no. 3, pp. 52–67, Jun. 2002.
  6. Sotirios P. Chatzis, Spyros Koukas, "Numerical optimization using synergetic swarms of foraging bacterial populations", Elsevier Expert Systems with Applications, vol. 38, Issue 12, pp. 15332-15343, Nov. -Dec. ,2011
  7. Hanning Chen, Yunlong Zhu, and Kunyuan Hu, "Adaptive Bacterial Foraging Optimization", Hindawi Publishing Corporation, Abstract and Applied Analysis, Volume 2011, Article ID 108269, 27 pages, doi:10. 1155/2011/108269.
Index Terms

Computer Science
Information Sciences

Keywords

Aso Pso Bfo Bfoa.