Improved Query Processing in Web Search Engines Using Grey Wolf Algorithm

  • Authors

    • Nishant Pal
    • Akshat Chawla
    • A Meena Priyadharsini
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12081
  • .

  • In Information systems working at a large scale where retrieval of information is an essential operation for example search engines etc. The users are not only concerned with the quality of results but also the time they consume for querying the data. These aspects lead to a natural tradeoff in which the approaches that lead to an increase in data have a similar larger response time and vice-versa. Hence, as the requirement for faster search query processing time along with efficient results is increasing, we need to identify other ways for increasing efficiency. This work proposes an application of the meta-heuristic algorithm called Grey Wolf Optimization (GWO) algorithm to improve Query Processing Time in Search Engines. The GWO algorithm is an alter ego of the way in which the grey wolves are organised and their hunting techniques. There are four categories of  grey wolves in a single pack of grey wolves which are alpha, beta, delta, and omega respectively. They are used to work in a simulating hierarchy. These help achieve better search results at decrease query response timings.

  • References

    1. [1] Zhihui Du∗, Hongyang Sun†, Yuxiong He‡, Yu He∗, David A. Bader§, Huazhe Zhang “Energy-Efficient Scheduling for Best-Effort Interactive Services to achieve high response qualityâ€

      [2] Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das School of Computer EngineeringI.†An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithmsâ€

      [3] Mahdi Mahjour-Bonab1 and Javad Sohafi-Bonab“Query Optimization in Grid Databases Using with Particle Swarm Optimizationâ€

      [4] Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer."

      [5] Craig Macdonald1, Nicola Tonellotto2, Iadh Ounis1 “Learning to Predict Response Times for Online Query Schedulingâ€

      [6] Xiao mingyao,Li Xiongfei “Embedded database query optimization algorithm based on particle swam optimizationâ€

      [7] M. N. Alpdemir, A. Mukherjee, and N. W. Paton, “Servicebased Distributed Querying on the Gridâ€. ICSOC 2003, First International Conference, Trento, Italy, 2003, PP.467-482.

      [8] N. Bruno, and S. Chaudhuri, “Exploiting Statistics on Query Expressions for Optimizationâ€. In SIGMOD 2002, Proceedings ACM SIGMOD International Conference on Management of Data, June 3-6 2002.

      [9] S. Shivle, and H. J. Siege, “Mapping of Subtasks with Multiple Versions in a Heterogeneous Ad Hoc Grid Environment†Third International Symposium on Parallel and distributed computing, 2004,PP.380-387

      [10] Z. Zhou, “Using Heuristic and Genetic Algorithms for Large-Scale Database Journal of Information and Optimization.†Computing Science, Vol. 2, No. 4, 2007, PP.261-280.

      [11] J. Wang, J. Horng, Y. Hsu, and B. Liu, “A Genetic Algorithm for Set Query Optimization in Distributed Database Systems.â€, IEEE, 1996, PP.1977-1982.

      [12] A. Goyal, L. Vasiliu, and B. Sapkota, “Use of AI Query Optimization of Relational Database.â€, in 18th IEEE International Conference on tools with Artificial Intelligence (ICTAI06), 2006.

      [13] J. Kennedy, and Eberhart, Swarm Intelligence, Morgan Kaufman, 2001.

      [14] Z. J. Li, X. D. Liu,X. D. Duan, and C. R. Wang, “Optimal Solution for Grid Resource Allocation Usig Particle Swarm Optimizationâ€, Third International Conference on Multimedia and Ubiquitous Engineering, 2009, PP.339-346.

      [15] V. Zadorozhny, A. Gal, and L. Raschid, “AreNA: Adaptive Distributed Catalog Infrastructure Based on Relevence Network.â€, In the 31st VLDB Conference, Trondheim, Norway, 2005.

      [16] PlanetLab, PlanetLab Platform, http://www.planet-lab.org.

      [17] L. Shuo, and H. K. Karimi, “Grid Query Optimizer to Improve Query Processing in Gridsâ€, Future Generation Computer Systems, Vol.24, 2008,PP.342-353.

      [18] M. Lovbjerg, “Improving Particle Swarm optimization by Hybridization of Stochastic Search Heuristics and Self-Organized Criticalityâ€, M.S. thesis, supervisor: T.Krink, university of Aarhus, May 2002.

      [19] M. Clerc, and J. kennedy, "The Particle Swarm Explosion, Stability and Convergence in A Multidimensional Complex Space", in IEEE Transaction on Evolutionary computation, Vol.6, Feb 2002, PP.58-73.

      [20] J. Riget, and S. Vesterstrom, “A Diversity- Guided Particle Swarm Optimizer The ARPSOâ€, in journal of Evalife Technical Report, Vol.2, 2002.

      [21] Y. Shi, and R. Eberhart, “Empirical Study of Particle Swarm Optimizationâ€, In Proceedings of Congress on Evolutionary Computation, 1999, PP.1945-1950.

      [22] Z. Jirong, “A Modified Particle Swarm Optimization Algorithmâ€, Journal of Computers, Vol.4, No.12, 2009, PP.1231-1236.

      [23] Shu_Chuan, C. Jui_Fang, and P. Jui_fang, “A Parallel Particle Swarm Optimization Algorithm With Communication Strategiesâ€. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 2, November 2012.

      [24] T. Padmapriya and V. Saminadan, “Distributed Load Balancing for Multiuser Multi-class Traffic in MIMO LTE-Advanced Networksâ€, Research Journal of Applied Sciences, Engineering and Technology (RJASET) - Maxwell Scientific Organization , ISSN: 2040-7459; e-ISSN: 2040-7467, vol.12, no.8, pp:813-822, April 2016.

      [25] S.V.Manikanthan and K.Baskaran “Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Networkâ€, CiiT International Journal of Programmable Device Circuits and Systems,Print: ISSN 0974 – 973X & Online: ISSN 0974 – 9624, Issue : November 2011, PDCS 112011008.

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  • How to Cite

    Pal, N., Chawla, A., & Meena Priyadharsini, A. (2018). Improved Query Processing in Web Search Engines Using Grey Wolf Algorithm. International Journal of Engineering & Technology, 7(2.24), 353-357. https://doi.org/10.14419/ijet.v7i2.24.12081