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A Study of Bacterial Foraging Optimization Algorithm and its Applications to Solve Simultaneous Equations

by Gautam Mahapatra, Soumya Banerjee
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 5
Year of Publication: 2013
Authors: Gautam Mahapatra, Soumya Banerjee
10.5120/12487-7927

Gautam Mahapatra, Soumya Banerjee . A Study of Bacterial Foraging Optimization Algorithm and its Applications to Solve Simultaneous Equations. International Journal of Computer Applications. 72, 5 ( June 2013), 1-6. DOI=10.5120/12487-7927

@article{ 10.5120/12487-7927,
author = { Gautam Mahapatra, Soumya Banerjee },
title = { A Study of Bacterial Foraging Optimization Algorithm and its Applications to Solve Simultaneous Equations },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 5 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number5/12487-7927/ },
doi = { 10.5120/12487-7927 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:05.799482+05:30
%A Gautam Mahapatra
%A Soumya Banerjee
%T A Study of Bacterial Foraging Optimization Algorithm and its Applications to Solve Simultaneous Equations
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 5
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For the solution of a set equation (linear or non-linear) with n number (n > 1) of variables we need at least n number of different relations (called as rank). Our present work is showing how the bio-inspired Bacteria Foraging Optimization Algorithm (BFOA), which is mimicry of the life-cycle of common type of bacteria like E. Coli, can be used to solve such system of equation with rank less than or equal to n. The BFOA simulates efficient nutrient foraging technique called as Chemotaxis to maximize the intake energy per unit time spend, the reproduction for evolution and the elimination-dispersal for environmental changes like any kind of natural calamities that are observed in the Bacterial system. As a sample tests we have used a numbers of system of linear equations with rank equal to the number of variables and a system of non-linear equations used in the derivation process of 4th order Runge-Kutta method for the ordinary differential equation solution, and experimental results are showing the applicability of the BFOA and in case of Runge-Kutta method we present an alternative form of the recursive equation.

References
  1. A. Abraham, A. Biswas, and S. Dasgupta et al. , Analysis of reproduction operator in bacterial foraging optimization algorithm, In CEC 2008: IEEE World Congress on Computational Intelligence, IEEE Press, pages 1476-1483, Hong Kong, June, 2008.
  2. Badamchizadeh, M. A. , Nikdel,A. , Kouzehgar, M. , 'Comparison of Genetic algorithm and particle swarm optimization for data fusion method based on kalman filter' International Journal of Artificial Intelligence,5(10), pp 67-78, 2010.
  3. C. Wu, N. Zhang, J. Jiang, J. Yang, and Y. Liang, Improved bacterial foraging algorithms and their applications to job shop scheduling problems, B. Beliezynski et al. (Eds. ): ICANNGA 2007, Part-I, LNCS 4431, pp. 562-569, 2007.
  4. D. H. Kim and C. H. Cho, Bacterial foraging based neural network fuzzy learning, In IICAI 2005: Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IEEE Press, pp. 2030-2036, Pune, India , December, 2005.
  5. D. H. Kim, A. Abraham, and J. H. Cho, A hybrid genetic algorithm and bacterial foraging approach for global optimization, Information Science 177 (18), pp. 3918-3937, 2007.
  6. H. Chen, Y. Zhu, and K. Hu, Adaptive bacterial foraging optimization, Abstract and Applied Analysis, vol. 2011, Article ID 108269, 27 pages,2011.
  7. H. Chen, Y. Zhu, and K. Hu, Self-Adaptation in bacterial foraging optimization algorithm, In ICISKE 2008: Proceeding of 3rd International Conference on Intelligent System and Knowledge Engineering, IEEE Press, pp. 1026-1031,2008.
  8. H. Chen, Y. Zhu, and K. Hu, Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning, Applied Soft Computing 10(2010), pp. 539-547,2009.
  9. H. Chen, Y. Zhu, and K. Hu, Cooperative bacteria foraging optimization, Discrete Dynamics in Nature and Society, vol. 2009, Article ID 815247, 17 pages, 2009.
  10. H. Chen, Y. Zhu, K. Hu, and T. Ku, Dynamic RFID network optimization using a self-adaptive bacterial foraging algorithm, International Journal of Artificial Intelligence, vol. 7, no. A11, pp. 219-231, 2011.
  11. H. Shen, Y. Zhu, X. Zhou, H. Guo, and C. Chang, Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization, In GEC 2009: Proceedings of Global Evolutionary Computing, ACM Press, pp. 497-504, Shanghai, China, June, 2009.
  12. Ikotun Abiodun M, Lawal Olawale N, and Adelokun adebowale P, The effectiveness of genetic algorithm in solving simultaneous equations, International Journal of Computer Applications (0975-8887), vol. 14, pp. 38-41, February, 2011.
  13. J. H. Mathews, Numerical Methods for Mathematics, Science and Engineering, 2nd Edition, PHI Prentice-Hall India, 2005.
  14. K. M. Passino, Biomimicry of Bacterial Foraging for distributed optimization and control, IEEE Control System Magazine, vol. 22, no. 2, pp. 52-67, 2002.
  15. L. Ulagammai, P. Vankatesh, and P. S. Kannan et al. , Application of bacteria foraging technique trained and artificial and wavelet neural networks in load forecasting, Neurocomputing, 70(16-18), pp. 2659-2667, 2007.
  16. M. S. Li, T. Y. Ji, W. J. Tang, Q. W. Wu, and J. R. Saunders, Bacterial foraging algorithm with varying population, BioSystems 100 (2010), pp. 185-197, 2010.
  17. M. Tripathy, S. Mishra, I. L. Lai, and Q. P. Zhang, Transmission loss reduction based on FACTS and bacterial foraging algorithm, In PPSN 2006: Proceeding of the 9th International Conference on Parallel Problem Solving from Nature, vol. 4193 of lecture notes in Computer Science, pp. 222-231, September, 2006.
  18. N. Kushwaha, V. S. Bisht and G. Shah, Genetic algorithm based bacterial foraging approach for optimization, National Conference on Future Aspects of AI in Industrial Automation (NCFAAIIA 2012), Proceedings published by International Journal of Computer Applications (IJCA), (0975-8887), vol. 17, pp. 11-14, September, 2012.
  19. S. Dasgupta, S. Das, A. Abraham, and A. Biswas, Adaptive computational chemotaxis in bacterial foraging optimization: an analysis, IEEE Transactions on Evolutionary Computing, vol. 13, no. 4, pp. 919-941, 2009.
  20. S. Das, S. Dasgupta, and A. Biswas et al. , On stability of the chemotactic dynamics in bacterial foraging, IEEE Transactions on System, Man and Cybernetics Part A: Systems and Humans, vol. 39, no. 3, pp. 670-679, May, 2009.
  21. S. Mishra, A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation, IEEE Transactions on Evolutionary Computation, vol. 9, no. 1, pp. 61-73, 2005.
  22. S. Mishra and C. N. Bhende, Bacterial foraging technique based optimized active power filter for load compensation, IEEE Transactions on Power Delivery, vol. 22, no. 1, pp. 457-465, 2007.
  23. S. Gilbert, 2007, Linear Algebra and its Applications. Pacific Grove: Brooks Cole
  24. S. S. Patnaik and A. K. Panda, Particle swarm optimization and bacterial foraging optimization techniques for optimal current harmonic mitigation by employing active power filter, Applied Computational Intelligence and Soft Computing, vol. 2012, Article ID 897127, 10 pages, 2012.
  25. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes The art of scientific computing, 3rd Edition, Cambridge University Press, 2007.
  26. W. Korani, Bacterial foraging oriented by particle swarm optimization strategy for PID tuning, In GECCO 2008: Proceedings of the Genetic and Evolutionary Computation Conference, ACM Press, pp. 1823-1826, Atlanta, GA,USA, July, 2008.
  27. W. J. Tang, Q. H. Wu, and J. R. Saunders, Bacterial foraging algorithm for dynamic environments, In CEC 2006: IEEE Congress on Evolutionary Computation, IEEE Press, pp. 1324-1330,BC, Canada, July, 2006.
  28. Y. Liu and K. M. Passino, Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors, Journal of Optimization Theory and Applications. 115(3): 603-628, December, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

BFOA chemotaxis tumble swim swarm reproduction elimination and dispersion Total Square Errors BFOASimulation

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