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PBBO: A New Approach for Underground Water Analysis

by Harish Kundra, Sumit Kaur, Ashish Verma, Dr. R.P.S Bedi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 30 - Number 3
Year of Publication: 2011
Authors: Harish Kundra, Sumit Kaur, Ashish Verma, Dr. R.P.S Bedi
10.5120/3620-5055

Harish Kundra, Sumit Kaur, Ashish Verma, Dr. R.P.S Bedi . PBBO: A New Approach for Underground Water Analysis. International Journal of Computer Applications. 30, 3 ( September 2011), 37-40. DOI=10.5120/3620-5055

@article{ 10.5120/3620-5055,
author = { Harish Kundra, Sumit Kaur, Ashish Verma, Dr. R.P.S Bedi },
title = { PBBO: A New Approach for Underground Water Analysis },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 30 },
number = { 3 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume30/number3/3620-5055/ },
doi = { 10.5120/3620-5055 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:00.748728+05:30
%A Harish Kundra
%A Sumit Kaur
%A Ashish Verma
%A Dr. R.P.S Bedi
%T PBBO: A New Approach for Underground Water Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 3
%P 37-40
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

From last ten to fifteen years many optimization techniques have been evolved and enhanced. Out of those, PSO and BBO are the two techniques that have been widely used in Swarm Optimization. PSO is better in comparison many genetic algorithms. PSO has applications in various areas like Optimization, Neural Networks training, Fuzzy controls and etc. BBO is based on science of biogeography. BBO has some features common to PSO and Genetic algorithms but it has some important features that make it more reliable than others. In this paper, we have proposed new algorithm that combines the features of PSO and BBO. It will help in providing more reliability in optimization world.

References
  1. J. Kennedy, R. C. Eberhart, and Y.Shi, Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco, 2001.
  2. H. Sun, Y. Pan, and Y. Zhang, “Pso based gabor wavelet feature extraction method,” in Proceedings of the Intern’l Conference on Information Acquisition, 21-25 June 2004, pp. 422 – 425, 2005.
  3. “Image Enhancement Using Particle Swarm Optimization”, Malik Braik, Alaa Sheta and Aladdin Ayesh, Proceedings of the World Congress on Engineering 2007 Vol I. WCE 2007, July 2 - 4, 2007, london, U.K.
  4. “Biogeography Based Groundwater Exploration”, V.K.Panchal, Harish Kundra, Amanpreet Kaur, IJCA, 2010.
  5. “A Probabilistic Analysis of a Simplified Biogeography - Based Optimization Algorithm”, Dan Simon Cleveland, State University Department of Electrical and Computer Engineering Cleveland, Ohio 44115 February 13, 2009.
  6. “Comparative Analysis in Medical Imaging”, Ashish Verma, Bharti Sharma, IJCA, 2010.
  7. “Study on the behviour of BBO over terrain features”, V.K Panchal, Divya Bhugra, Samiksha Goel, Vipul Singhania, IEEE 2011.
  8. F Vanden Bergh, “An Analysis of Particle Swarm Optimizers”, PhD Thesis, University of Pretoria, South Africa, 2002.
  9. Kundra Harish and Monica Sood(2010) “Cross-Country Path Finding using Hybrid approach of PSO and BBO” International Journal of Computer Applications (0975 – 8887), vol. 7, pp. 6.
  10. Magnus E. H. pedersen, Hvass Lab (2010), “Good parameters for particle swarm optimization”.
  11. Magnus E. H. pedersen, “SwarmOps for matlab: Numeric & heuristic optimization”, Hvass Lab, 2011
  12. Panchal V.K, Goel S. and Bhatnagar M., (2009), “Biogeography Based Land Cover Feature Extraction”, IEEE, pp. 1588- 1591.
Index Terms

Computer Science
Information Sciences

Keywords

PSO BBO GA Optimization Underground Water Exploration

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