CFP last date
22 April 2024
Reseach Article

A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering

by Suresh Chittineni, P.V.G.D. Prasad Reddy, Suresh Chandra Satapathy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 8
Year of Publication: 2016
Authors: Suresh Chittineni, P.V.G.D. Prasad Reddy, Suresh Chandra Satapathy
10.5120/ijca2016909423

Suresh Chittineni, P.V.G.D. Prasad Reddy, Suresh Chandra Satapathy . A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering. International Journal of Computer Applications. 140, 8 ( April 2016), 27-32. DOI=10.5120/ijca2016909423

@article{ 10.5120/ijca2016909423,
author = { Suresh Chittineni, P.V.G.D. Prasad Reddy, Suresh Chandra Satapathy },
title = { A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 8 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number8/24615-2016909423/ },
doi = { 10.5120/ijca2016909423 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:51.339164+05:30
%A Suresh Chittineni
%A P.V.G.D. Prasad Reddy
%A Suresh Chandra Satapathy
%T A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 8
%P 27-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data clustering is one of the data mining task, it is used to group the data objects according to their similarity. It is an optimization problem to find optimal results apply the proposed parallel approach called P-AISFLA. This hybrid algorithm is developed by utilizing the benefits of both social and immune mechanisms. The social algorithm Shuffled Frog Leaping Algorithm is a new parameter free population based algorithm combined with Clonal selection algorithm CSA. This hybrid algorithm performs the parallel computation of social behavior based SFLA and Immune behavior based CSA to improve the ability to reach the global optimal solution with a faster and a rapid convergence rate. The proposed algorithm PAISFL is applied for the data clustering applications and proved that it produces optimal results than SFLA and PSO.

References
  1. A. Jain, M. Murty, and P. Flynn, “Data clustering: A review,” ACM Comput. Surv., vol. 31, no. 3, 1999, pp. 264–323.
  2. M. Halkidi and M. Vazirgiannis, “Clustering validity assessment: Finding the optimal partitioning of a data set,” in Proc. IEEE ICDM, San Jose, CA, 2001, pp. 187–194.
  3. Tapas Kanungo, Nathan S. Netanyahu, Angela Y. Wu “An efficient K-means clustering algorithm: analysis and implementation” IEEE Transaction on pattern analysis and machine intelligence, vol 24, No-27,pp 881-892,july 2002.
  4. K. Krishna and M. Murty, “Genetic K-means algorithm,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 29, no. 3, Jun. 1999,pp. 433–439.
  5. Raghavan,V. V., Birchand, K., Paterlinia, S. &Krink, T. (2006). Differential evolution and particle swarm optimization in partitional clustering. Comput. Stat. Data Anal.50, no. 5, 1220–1247.
  6. Emad Elbeltagi, Tarek Hegazy, and Donald Grierson, “Comparison among five evolutionary-based optimization algorithms”, Advanced Engineering Informatics, 2005 (19), pp. 43-53.
  7. M. M. Eusuff, K. E. Lansey, F. Pasha, “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization,” Engineering Optimization, 2006, vol. 38, no. 2, pp. 129-154.
  8. Leandro N. de Castro and Fernando J. Von Zuben,”Learning and Optimization Using the Clonal Selection Principle”, IEEE Trans. on Evolutionary Computation, Vol. 6, No. 3, JUNE 2002, pp. 239-251.
  9. De Castro and Jonathan Timmis. An Introduction to Artificial Immune Systems:A New Computational Intelligence Paradigm, Springer Verlag, 2002.
  10. Liong S-Y, Atiquzzaman Md. Optimal design of water distribution network using shuffled complex evolution. J Inst Eng, Singapore 2004;44(1):93–107.
  11. Xuncai Zhang1,2, Xuemei Hu3, Guangzhao Cui2, Yanfeng Wang2, Ying Niu2,” An Improved Shuffled Frog Leaping Algorithm with Cognitive Behavior”, Proceedings of the 7th World Congress on Intelligent Control and Automation June 25 - 27, 2008, Chongqing, China.
  12. Nareli Cruz-Cortes, Daniel Trejo-perez,A.Coello Coello, “Handling Constraints in Global Optimization using an Artificial Immune System”.ICARIS-2005,Springer LNCS,pp 234-247,2005.
  13. Jonathan Timmis, C.Edmonds and Kelsey, "Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for Function Optimisation," Proceedings of the Congress on Evolutionary Computation, 2004, pp.1044-1 051.
  14. Lijun Pan and Z Fu, A Clonal Selection Algorithm for Open Vehicle Routing Problem: Proceedings of third International Conference on Genetic and Evolutionary Computing, 2009.
  15. XQ Zuo and YS Fan,”A chaos search immune algorithm with its application to neuro-fuzzy controller design”, Chaos, Solitons and Fractals ;30 (2006) 94–109.
  16. Suganthan and S. Baskar ,”Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions”, IEEE Trans. on Evolutionary Computation, , Vol. 10, No. 3, June 2006.
  17. S. H. Ling and C. Iu,” Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications”, IEEE Tran. On Systems, Man and Cybernetics-Part B: Cybernetics, Vol. 38, No. 3, June 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Data clustering Shuffled Frog Leaping Algorithm (SFLA) CLONALG and P-AISFLA.