Email this article 
Open Access
Subscription or Fee Access
Network and Web Optimization Techniques Using Quantum Inspired Particle Swarm Intelligence
Abstract
Web Caching is a widely used technique to save bandwidth, to reduce server workload and to improve user response time, i.e., it improves the performance of web architectures. Web caching is a well-known approach for enhancing the performance of web based system by keeping web objects that are likely to be used in the near future in location closer to user. The web caching mechanisms are implemented at three levels: client level, proxy level and original server level. Significantly, proxy servers play the key roles between users and web sites in lessening of the response time of user requests and saving of network bandwidth. Therefore, for achieving better response time, an efficient caching approach should be built in a proxy server. This paper presents a new variant of Basic Particle Swarm Optimization (BPSO) algorithm named QI-PSO for improving the performance. The QI-PSO algorithm makes use of a multi-parent, quadratic crossover/reproduction operator defined in the BPSO algorithm. The proposed algorithm is compared it with BPSO and the experimental results show that QIPSO outperforms the BPSO algorithm.
Keywords
References
Ho, Li Hsing, Meng Huang Lu, Hui Yi Ho, and Tien Fu Peng. "A study of website optimization strategy and implementation." Advanced Materials Research 268 (2011): 829-834.
Sivaji, Ashok, Azween Abdullah, and Alan G. Downe. "Usability testing methodology: Effectiveness of heuristic evaluation in E-government website development." In Modelling Symposium (AMS), 2011 Fifth Asia, pp. 68-72. IEEE, 2011.
Buffone, Robert, Raymond Stata, and Coach Wei. "System and method for website performance optimization and internet traffic processing." U.S. Patent 8,112,471, issued February 7, 2012.
Helbig, Mardé, and Andries P. Engelbrecht. "Dynamic multi-objective optimization using PSO." In Metaheuristics for Dynamic Optimization, pp. 147-188. Springer Berlin Heidelberg, 2013.
Zhao, Xinchao, Boqian Song, Panyu Huang, Zichao Wen, Jialei Weng, and Yi Fan. "An improved discrete immune optimization algorithm based on PSO for QoS-driven web service composition." Applied Soft Computing 12, no. 8 (2012): 2208-2216.
Chang, Jui-Fang, and Pei-Yu Hsieh. "Particle swarm optimization based on back propagation network forecasting exchange rates." International Journal of Innovative Computing, Information and Control 7, no. 12 (2011): 6837-6847.
Lee, Zne-Jung, Kuo-Ching Ying, Shih-Chieh Chen, and Shih-Wei Lin. "Applying PSO-based BPN for predicting the yield rate of DRAM modules produced using defective ICs." The International Journal of Advanced Manufacturing Technology49, no. 9-12 (2010): 987-999.
Isvarya Luckshmi, A. P., P. Visalakshi, and N. K. Karthikeyan. "Intelligent Schemes for Bandwidth Allocation in Cellular Mobile Networks." In Process Automation, Control and Computing (PACC), 2011 International Conference on, pp. 1-6. IEEE, 2011.
Malviya, Rakesh, and Dilip Kumar Pratihar. "Tuning of neural networks using particle swarm optimization to model MIG welding process." Swarm and Evolutionary Computation 1, no. 4 (2011): 223-235.
Kuo, R. J., S. Y. Hong, and Y. C. Huang. "Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection." Applied Mathematical Modelling 34, no. 12 (2010): 3976-3990.
Che, Z. H. "PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding." Computers & Industrial Engineering 58, no. 4 (2010): 625-637.
Tripathy, Manoj, Rudra Prakash Maheshwari, and H. K. Verma. "Power transformer differential protection based on optimal probabilistic neural network."Power Delivery, IEEE Transactions on 25, no. 1 (2010): 102-112.
Zendehboudi, Sohrab, Mohammad Ali Ahmadi, Lesley James, and Ioannis Chatzis. "Prediction of condensate-to-gas ratio for retrograde gas condensate reservoirs using artificial neural network with particle swarm optimization."Energy & Fuels 26, no. 6 (2012): 3432-3447.
Lazzús, Juan A. "Prediction of flash point temperature of organic compounds using a hybrid method of group contribution+ neural network+ particle swarm optimization." Chinese Journal of Chemical Engineering 18, no. 5 (2010): 817-823.
Geethanjali, M., S. Mary Raja Slochanal, and R. Bhavani. "PSO trained ANN-based differential protection scheme for power transformers." Neurocomputing71, no. 4 (2008): 904-918.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.