Authors: B. Kaushik; H. Banka
Addresses: Computer Science Department, Krishna Engineering College, 95, Loni Road, Ghaziabad, 201012, UP, India ' Department of Computer Science & Engineering, Indian School of Mines, Dhanbad-826004, India
Abstract: This paper present an optimal artificial neural network approach for improving upper bound on link reliability in optimal network design. Improving reliability in a growing variable-sized network is an important parameter for optimal network design. Many alternative methods for improving reliability have been used for optimal network design. Most of these methods, mentioned in the literature are simulation-based. These methods provide simple ways for measuring reliability when networks have limited size. These methods require significant computational effort and time for growing variable-sized networks. An optimal neural network method is therefore proposed for reliability improvement in optimal network design. The proposed algorithm has two phases: experimental setup and optimal phase. Experimental setup phase scans all possible network topologies for reliability measures. And, optimal phase constructs optimal network design with improved reliability upper-bound. Both neural networks were studied with fixed and varying links. Results are grouped using cross-validation method showing that the optimised artificial neural network approach gives precise measures for significant reliability improvement than the upper-bound than heuristic-based approach. Results show that the optimised ANN produces optimal network designs and reliability measures at reasonable computational cost.
Keywords: reliability improvement; optimal design; network design; variable-sized networks; artificial neural networks; ANNs.
International Journal of Advanced Intelligence Paradigms, 2014 Vol.6 No.3, pp.157 - 175
Received: 06 Jul 2013
Accepted: 21 Feb 2014
Published online: 29 Oct 2014 *