CFP last date
20 June 2024
Reseach Article

A Novel Web Optimization Technique using Enhanced Particle Swarm Optimization

Published on June 2015 by P.n.nesarajan, M.venkatachalam, T.ranganayaki
National Conference on Research Issues in Image Analysis and Mining Intelligence
Foundation of Computer Science USA
NCRIIAMI2015 - Number 1
June 2015
Authors: P.n.nesarajan, M.venkatachalam, T.ranganayaki
030cec94-c1ce-42ea-b729-0b9fd2e28c4d

P.n.nesarajan, M.venkatachalam, T.ranganayaki . A Novel Web Optimization Technique using Enhanced Particle Swarm Optimization. National Conference on Research Issues in Image Analysis and Mining Intelligence. NCRIIAMI2015, 1 (June 2015), 28-32.

@article{
author = { P.n.nesarajan, M.venkatachalam, T.ranganayaki },
title = { A Novel Web Optimization Technique using Enhanced Particle Swarm Optimization },
journal = { National Conference on Research Issues in Image Analysis and Mining Intelligence },
issue_date = { June 2015 },
volume = { NCRIIAMI2015 },
number = { 1 },
month = { June },
year = { 2015 },
issn = 0975-8887,
pages = { 28-32 },
numpages = 5,
url = { /proceedings/ncriiami2015/number1/21021-4012/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Research Issues in Image Analysis and Mining Intelligence
%A P.n.nesarajan
%A M.venkatachalam
%A T.ranganayaki
%T A Novel Web Optimization Technique using Enhanced Particle Swarm Optimization
%J National Conference on Research Issues in Image Analysis and Mining Intelligence
%@ 0975-8887
%V NCRIIAMI2015
%N 1
%P 28-32
%D 2015
%I International Journal of Computer Applications
Abstract

Web performance is very important. One way to improve performance is through caching. But caching is already widely used, and studies suggest that much of the theoretically achievable performance from caching is already being realized. Caching has reduced bandwidth consumption and downloads latency. On the other hand, web-caching is heavy to enlarge further due to the developing amount of non-cacheable dynamic web-documents. Increasing the performance of web is an essential requirement, because its result in a huge increase in user supposed latency. This neat source of information establishes a basis for observations that can lead to improved overall performance for a given Web site. The main limitation focused in this method is to find out the optimal cache memory that should be keeping in order achieving maximum cost effectiveness. This method utilizes a successful Great Deluge algorithm based Particle Swarm Optimization (GDPSO) approach for achieving the best cache memory size which in turn decreases all the network cost. The investigation shows that hierarchical distributed caching can save important network cost through the use of the GDPSO algorithm.

References
  1. Abd-Elazim, S. M. , and Ali, E. S. 2014. A hybrid particle swarm optimization and bacterial foraging for power system stability enhancement. Complexity.
  2. Acan, A. , and Ünveren, A. 2014. A two-stage memory powered Great Deluge algorithm for global optimization. Soft Computing, 1-21
  3. Banos, R. , Manzano-Agugliaro, F. , Montoya, F. G. , Gil, C. , Alcayde, A. , and Gómez, J. 2011. Optimization methods applied to renewable and sustainable energy: A review. Renewable and Sustainable Energy Reviews, 15(4), 1753-1766.
  4. Jahani, E. , Cafarella, M. J. , and Ré, C. 2011. Automatic optimization for MapReduce programs. Proceedings of the VLDB Endowment, 4(6), 385-396.
  5. Jang, Y. , and Jovanovic, M. M. 2010. Light-load efficiency optimization method. Power Electronics, IEEE Transactions on, 25(1), 67-74.
  6. Jordehi, A. R. 2014. Particle swarm optimisation for dynamic optimisation problems: a review. Neural Computing and Applications, 25(7-8), 1507-1516.
  7. Kaveh, A. 2014. Particle Swarm Optimization. In Advances in Metaheuristic Algorithms for Optimal Design of Structures (pp. 9-40). Springer International Publishing.
  8. Kennedy, J. 2010. Particle swarm optimization. In Encyclopedia of Machine Learning (pp. 760-766). Springer US.
  9. Lizorkin, D. , Velikhov, P. , Grinev, M. , and Turdakov, D. 2010. Accuracy estimate and optimization techniques for SimRank computation. The VLDB Journal—The International Journal on Very Large Data Bases, 19(1), 45-66.
  10. Ouyang, A. , Li, K. , Truong, T. K. , Sallam, A. , and Sha, E. H. M. 2014. Hybrid particle swarm optimization for parameter estimation of Muskingum model. Neural Computing and Applications, 25(7-8), 1785-1799.
  11. Poli, R. , Kennedy, J. , and Blackwell, T. 2007. Particle swarm optimization. Swarm intelligence, 1(1), 33-57.
  12. Sendra Compte, S. , Lloret, J. , García Pineda, M. , and Toledo Alarcón, J. F. 2011. Power saving and energy optimization techniques for Wireless Sensor Networks. Journal of communications, 6(6), 439-459.
  13. Sulaiman, S. , Shamsuddin, S. M. , Forkan, F. , and Abraham, A. 2008, May. Intelligent Web caching using neurocomputing and particle swarm optimization algorithm. In Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on (pp. 642-647). IEEE.
  14. Unler, A. , and Murat, A. 2010. A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research, 206(3), 528-539.
  15. Yusup, N. , Zain, A. M. , and Hashim, S. Z. M. 2012. Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011). Expert Systems with Applications, 39(10), 9909-9927.
  16. Zhang, Z. , Jiang, Y. , Zhang, S. , Geng, S. , Wang, H. , and Sang, G. 2014. An adaptive particle swarm optimization algorithm for reservoir operation optimization. Applied Soft Computing, 18, 167-177.
Index Terms

Computer Science
Information Sciences

Keywords

Website Optimization Techniques Web Performance Particle Swarm Optimization (pso) Great Deluge Particle Swarm Optimization (gdpso)